8,725 machine learning methods and techniques
Stable Rank Normalization
Stable Rank Normalization (SRN) is a weight-normalization scheme which minimizes the stable rank of a linear operator. It simultaneously controls the Lipschitz constant and the stable rank of a linear operator. Stable rank is a softer version of the rank operator and is defined as the squared ratio of the Frobenius norm to the spectral norm.
Local Prior Matching
Local Prior Matching is a semi-supervised objective for speech recognition that distills knowledge from a strong prior (e.g. a language model) to provide learning signal to a discriminative model trained on unlabeled speech. The LPM objective minimizes the cross entropy between the local prior and the model distribution, and is minimized when . Intuitively, LPM encourages the ASR model to assign posterior probabilities proportional to the linguistic probabilities of the proposed hypotheses.
SNIP, or Scale Normalization for Image Pyramids, is a multi-scale training scheme that selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. SNIP is a modified version of MST where only the object instances that have a resolution close to the pre-training dataset, which is typically 224x224, are used for training the detector. In multi-scale training (MST), each image is observed at different resolutions therefore, at a high resolution (like 1400x2000) large objects are hard to classify and at a low resolution (like 480x800) small objects are hard to classify. Fortunately, each object instance appears at several different scales and some of those appearances fall in the desired scale range. In order to eliminate extreme scale objects, either too large or too small, training is only performed on objects that fall in the desired scale range and the remainder are simply ignored during back-propagation. Effectively, SNIP uses all the object instances during training, which helps capture all the variations in appearance and pose, while reducing the domain-shift in the scale-space for the pre-trained network.
Iterative Pseudo-Labeling
Iterative Pseudo-Labeling (IPL) is a semi-supervised algorithm for speech recognition which efficiently performs multiple iterations of pseudo-labeling on unlabeled data as the acoustic model evolves. In particular, IPL fine tunes an existing model at each iteration using both labeled data and a subset of unlabeled data.
ALBEF introduces a contrastive loss to align the image and text representations before fusing them through cross-modal attention. This enables more grounded vision and language representation learning. ALBEF also doesn't require bounding box annotations. The model consists of an image encode, a text encoder, and a multimodal encoder. The image-text contrastive loss helps to align the unimodal representations of an image-text pair before fusion. The image-text matching loss and a masked language modeling loss are applied to learn multimodal interactions between image and text. In addition, momentum distillation is used to generate pseudo-targets. This improves learning with noisy data.
AdaDelta is a stochastic optimization technique that allows for per-dimension learning rate method for SGD. It is an extension of Adagrad that seeks to reduce its aggressive, monotonically decreasing learning rate. Instead of accumulating all past squared gradients, Adadelta restricts the window of accumulated past gradients to a fixed size . Instead of inefficiently storing previous squared gradients, the sum of gradients is recursively defined as a decaying average of all past squared gradients. The running average at time step then depends only on the previous average and current gradient: Usually is set to around . Rewriting SGD updates in terms of the parameter update vector: AdaDelta takes the form: The main advantage of AdaDelta is that we do not need to set a default learning rate.
RESCAL
Go-Explore is a family of algorithms aiming to tackle two challenges with effective exploration in reinforcement learning: algorithms forgetting how to reach previously visited states ("detachment") and from failing to first return to a state before exploring from it ("derailment"). To avoid detachment, Go-Explore builds an archive of the different states it has visited in the environment, thus ensuring that states cannot be forgotten. Starting with an archive beginning with the initial state, the archive is built iteratively. In Go-Explore we: (a) Probabilistically select a state from the archive, preferring states associated with promising cells. (b) Return to the selected state, such as by restoring simulator state or by running a goal-conditioned policy. (c) Explore from that state by taking random actions or sampling from a trained policy. (d) Map every state encountered during returning and exploring to a low-dimensional cell representation. (e) Add states that map to new cells to the archive and update other archive entries.
Graph Contrastive learning with Adaptive augmentation
High-Order Proximity preserved Embedding
BigBird is a Transformer with a sparse attention mechanism that reduces the quadratic dependency of self-attention to linear in the number of tokens. BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. In particular, BigBird consists of three main parts: - A set of global tokens attending on all parts of the sequence. - All tokens attending to a set of local neighboring tokens. - All tokens attending to a set of random tokens. This leads to a high performing attention mechanism scaling to much longer sequence lengths (8x).
SRU, or Simple Recurrent Unit, is a recurrent neural unit with a light form of recurrence. SRU exhibits the same level of parallelism as convolution and feed-forward nets. This is achieved by balancing sequential dependence and independence: while the state computation of SRU is time-dependent, each state dimension is independent. This simplification enables CUDA-level optimizations that parallelize the computation across hidden dimensions and time steps, effectively using the full capacity of modern GPUs. SRU also replaces the use of convolutions (i.e., ngram filters), as in QRNN and KNN, with more recurrent connections. This retains modeling capacity, while using less computation (and hyper-parameters). Additionally, SRU improves the training of deep recurrent models by employing highway connections and a parameter initialization scheme tailored for gradient propagation in deep architectures. A single layer of SRU involves the following computation: where and are parameter matrices and and are parameter vectors to be learnt during training. The complete architecture decomposes to two sub-components: a light recurrence and a highway network, The light recurrence component successively reads the input vectors and computes the sequence of states capturing sequential information. The computation resembles other recurrent networks such as LSTM, GRU and RAN. Specifically, a forget gate controls the information flow and the state vector is determined by adaptively averaging the previous state and the current observation according to .
Decaying Momentum, or Demon, is a stochastic optimizer motivated by decaying the total contribution of a gradient to all future updates. By decaying the momentum parameter, the total contribution of a gradient to all future updates is decayed. A particular gradient term contributes a total of of its "energy" to all future gradient updates, and this results in the geometric sum, . Decaying this sum results in the Demon algorithm. Letting be the initial ; then at the current step with total steps, the decay routine is given by solving the below for : Where refers to the proportion of iterations remaining. Note that Demon typically requires no hyperparameter tuning as it is usually decayed to or a small negative value at time . Improved performance is observed by delaying the decaying. Demon can be applied to any gradient descent algorithm with a momentum parameter.
A CoordConv layer is a simple extension to the standard convolutional layer. It has the same functional signature as a convolutional layer, but accomplishes the mapping by first concatenating extra channels to the incoming representation. These channels contain hard-coded coordinates, the most basic version of which is one channel for the coordinate and one for the coordinate. The CoordConv layer keeps the properties of few parameters and efficient computation from convolutions, but allows the network to learn to keep or to discard translation invariance as is needed for the task being learned. This is useful for coordinate transform based tasks where regular convolutions can fail.
Chimera is a pipeline model parallelism scheme which combines bidirectional pipelines for efficiently training large-scale models. The key idea of Chimera is to combine two pipelines in different directions (down and up pipelines). Denote as the number of micro-batches executed by each worker within a training iteration, and the number of pipeline stages (depth), and the number of workers. The Figure shows an example with four pipeline stages (i.e. ). Here we assume there are micro-batches executed by each worker within a training iteration, namely , which is the minimum to keep all the stages active. In the down pipeline, stage∼stage are mapped to linearly, while in the up pipeline the stages are mapped in a completely opposite order. The (assuming an even number) micro-batches are equally partitioned among the two pipelines. Each pipeline schedules micro-batches using 1F1B strategy, as shown in the left part of the Figure. Then, by merging these two pipelines together, we obtain the pipeline schedule of Chimera. Given an even number of stages (which can be easily satisfied in practice), it is guaranteed that there is no conflict (i.e., there is at most one micro-batch occupies the same time slot on each worker) during merging.
Temporal Graph Network
Temporal Graph Network, or TGN, is a framework for deep learning on dynamic graphs represented as sequences of timed events. The memory (state) of the model at time consists of a vector for each node the model has seen so far. The memory of a node is updated after an event (e.g. interaction with another node or node-wise change), and its purpose is to represent the node's history in a compressed format. Thanks to this specific module, TGNs have the capability to memorize long term dependencies for each node in the graph. When a new node is encountered, its memory is initialized as the zero vector, and it is then updated for each event involving the node, even after the model has finished training.
21 Ways to Contact: How Can I Speak to Someone at Expedia, call +1-805-330-4056 or use the app’s live chat. Visit Expedia.com/contact +1-805-330-4056 to log in and request a callback from an agent. Expedia provides 24/7 assistance at +1-805-330-4056. Need quick support? Dial +1-805-330-4056or use the app chat for immediate help. Want a callback? Log in at Expedia.com/contact to request one. Expedia’s team is available at +1-805-330-4056. To reach Expedia support, call +1-805-330-4056 or use the app’s live chat. Visit Expedia.com/contact +1-805-330-4056 to log in and request a callback from an agent. Expedia provides 24/7 assistance at +1-805-330-4056. Need quick support? Dial +1-805-330-4056 or use the app chat for immediate help. Want a callback? Log in at Expedia.com/contact to request one. Expedia’s team is available at +1-805-330-4056. To reach Expedia support, call +1-805-330-4056 or use the app’s live chat. Visit Expedia.com/contact +1-805-330-4056 to log in and request a callback from an agent. Expedia provides 24/7 assistance at +1-805-330-4056. Need quick support? Dial +1-805-330-4056 or use the app chat for immediate help. Want a callback? Log in at Expedia.com/contact to request one. Expedia’s team is available at +1-805-330-4056. {247 Support}Ways to reach Expedia customer service live person by Phone Number ☎️ +1-888-829-0881 If you need immediate assistance with your Expedia Air booking, baggage, or flight status, you can contact Expedia Air Customer Service at ☎️ +1-888-829-0881. The airline provides 24/7 support, ensuring that passengers get quick resolutions to their queries. (Great Deal) What is the Cheapest Day to Book Flights with Expedia Air? ☎️ +1-888-829-0881 For budget-friendly travel, Tuesdays and Wednesdays are generally the cheapest days to book Expedia Air flights. To get the best deals, try booking at least 3-4 weeks in advance. For exclusive discounts and last-minute offers, call ☎️ +1-888-829-0881. 【Senior】 How to Get a Senior Discount on Expedia Air? ☎️ +1-888-829-0881 Expedia Air offers discounts for senior travelers in some cases. While these discounts are not always advertised, you can check availability and eligibility by calling ☎️ +1-888-829-0881. Booking early increases your chances of securing a senior fare. [AANCPApply] What is Expedia Air Name Change Policy? ☎️ +1-888-829-0881 If you made a mistake while booking a flight, Expedia Air allows minor name corrections for free within 24 hours. After this period, fees may apply. To make a name correction or full name change, contact customer service at ☎️ +1-888-829-0881. {CONTACT NOW} How Do I Call Expedia Air from Mexico? ☎️ +1-888-829-0881 For passengers traveling to or from Mexico, contacting Expedia Air is easy. Dial ☎️ +1-888-829-0881 for direct assistance with bookings, cancellations, or inquiries regarding your Expedia Air flight from Mexico. {Free Cancellation} Does Expedia Air Still Have Free Cancellation? ☎️ +1-888-829-0881 Yes! Expedia Air allows free cancellations within 24 hours of booking. However, if you need to cancel after that period, cancellation fees may apply depending on the fare type. For detailed cancellation policies, call ☎️ +1-888-829-0881. [Live Support] How Do I Speak to Someone at Expedia Air? ☎️ +1-888-829-0881 If you need immediate assistance, calling ☎️ +1-888-829-0881 connects you with a live agent at Expedia Air. You can also use their live chat option or visit an airport customer service desk. {Hidden Fees?} Does Expedia Air Have Cancellation Fees? ☎️ +1-888-829-0881 Expedia Air waives cancellation fees for some refundable tickets. However, non-refundable fares may incur fees. To check the specific cancellation charges for your booking, contact Expedia Air support at ☎️ +1-888-829-0881. 【Travel Budget】 What is the Cheapest Day to Fly on Expedia Air? ☎️ +1-888-829-0881 For the lowest fares on Expedia Air, book flights on Tuesdays, Wednesdays, and Saturdays. Avoid peak travel days like Fridays and Sundays. To compare fares and book your ticket at the lowest price, call ☎️ +1-888-829-0881. {Baggage Policy} How Many Bags Can I Bring on Expedia Air? ☎️ +1-888-829-0881 Expedia Air allows one free carry-on bag and one personal item. Checked baggage fees vary depending on the destination and fare class. To check your baggage allowance, call ☎️ +1-888-829-0881. [Delayed Flight?] How to Check Your Expedia Air Flight Status? ☎️ +1-888-829-0881 To check your Expedia Air flight status, visit the airline’s website or call customer service at ☎️ +1-888-829-0881 for real-time updates on delays, gate changes, and rescheduling options. [We’re Available] How to Modify Your Expedia Air Booking? ☎️ +1-888-829-0881 If you need to change your travel date, destination, or passenger details, you can modify your booking through the Expedia Air website or by calling ☎️ +1-888-829-0881. {Upgrade Guide} How to Get a Seat Upgrade on Expedia Air? ☎️ +1-888-829-0881 Expedia Air offers premium seat upgrades for an additional fee. You can request an upgrade during booking or at check-in. For last-minute seat upgrades, call ☎️ +1-888-829-0881. {Exclusive Deals} What Are the Best Days to Buy Expedia Air Tickets? ☎️ +1-888-829-0881 To get the best price on Expedia Air tickets, book flights on Tuesdays and Wednesdays when airlines release discounted fares. To check for the latest deals, call ☎️ +1-888-829-0881. {100% Refund?} How to Request a Refund from Expedia Air? ☎️ +1-888-829-0881 If your ticket is eligible for a refund, you can submit a request online or by calling ☎️ +1-888-829-0881. Refund processing times vary depending on the payment method and fare type. {No More Fees} How to Avoid Extra Charges on Expedia Air? ☎️ +1-888-829-0881 To avoid unnecessary fees, book directly through the Expedia Air website, pack within the baggage limits, and check-in online. For more tips, contact ☎️ +1-888-829-0881. [Customer Help] How Do I Contact Expedia Air by Phone? ☎️ +1-888-829-0881 For any inquiries related to bookings, baggage, flight changes, or refunds, call Expedia Air Customer Service at ☎️ +1-888-829-0881 for quick support. Final Thoughts Expedia Air provides excellent customer support, flexible booking options, and affordable fares. Whether you need help with name corrections, refunds, baggage policies, or last-minute cancellations, the 24/7 customer service team is available at ☎️ +1-888-829-0881.
Feature Matching is a regularizing objective for a generator in generative adversarial networks that prevents it from overtraining on the current discriminator. Instead of directly maximizing the output of the discriminator, the new objective requires the generator to generate data that matches the statistics of the real data, where we use the discriminator only to specify the statistics that we think are worth matching. Specifically, we train the generator to match the expected value of the features on an intermediate layer of the discriminator. This is a natural choice of statistics for the generator to match, since by training the discriminator we ask it to find those features that are most discriminative of real data versus data generated by the current model. Letting denote activations on an intermediate layer of the discriminator, our new objective for the generator is defined as: . The discriminator, and hence , are trained as with vanilla GANs. As with regular GAN training, the objective has a fixed point where G exactly matches the distribution of training data.
Surface Nomral-based Spatial Propagation
Inspired by the spatial propagation mechanism utilized in the depth completion task \cite{NLSPN}, we introduce a normal incorporated non-local disparity propagation module in which we hub NDP to generate non-local affinities and offsets for spatial propagation at the disparity level. The motivation lies that the sampled pixels for edges and occluded regions are supposed to be selected. The propagation process aggregates disparities via plane affinity relations, which alleviates the phenomenon of disparity blurring at object edges due to frontal parallel windows. And the disparities in occluded areas are also optimized at the same time by being propagated from non-occluded areas where the predicted disparities are with high confidence.
Progressive Neural Architecture Search
Progressive Neural Architecture Search, or PNAS, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to complex ones, pruning out unpromising structures as we go. At iteration of the algorithm, we have a set of candidate cells (each of size blocks), which we train and evaluate on a dataset of interest. Since this process is expensive, PNAS also learns a model or surrogate function which can predict the performance of a structure without needing to train it. We then expand the candidates of size into children, each of size . The surrogate function is used to rank all of the children, pick the top , and then train and evaluate them. We continue in this way until , which is the maximum number of blocks we want to use in a cell.
"To get a response from American Airlines call at +1 (801) 855-5905 or +1 (804) 853-9001, you can use several methods, including calling their customer service, using their online customer relations forms, emailing, or reaching out through social media . Here's a breakdown of the various options: Phone: For general inquiries and customer service, you can call their customer service hotline at +1 (801) 855-5905 or +1 (804) 853-9001. For urgent matters like flight changes, cancellations, or rebooking, calling is recommended as online portals may not provide immediate solutions. American Airlines offers 24/7 customer service through their phone lines +1 (801) 855-5905 or +1 (804) 853-9001. Online Customer Relations Form: You can submit compliments, concerns, and questions about past travel using their online Customer Relations form. This method is often faster than sending a letter through the mail. You'll need to provide your contact information, confirmation code or ticket number, flight number, date of travel, and origin and destination. Email: For less urgent matters, you can email American Airlines, though response times may be longer. Be sure to include details like your record locator or ticket number +1 (801) 855-5905 or +1 (804) 853-9001, mailing address, flight numbers, origin and destination, travel dates, and a full explanation of your reason for contacting them. For international customer relations, use the email address International.CustomerRelations@aa.com and include ""UKInquiry-"" in the subject line if applicable. Social Media: American Airlines is active on social media platforms like Twitter (@AmericanAir) and Facebook. Many customers find that messaging via social media can lead to quick responses. Their Twitter account is monitored 24/7, according to their official Twitter account. Live Chat (via website or mobile app): You can use the live chat feature available on their website (in the Help section) or through the American Airlines mobile app to connect with a live representative. At the Airport:+1 (801) 855-5905 or +1 (804) 853-9001 If you're already at the airport, you can try contacting them in person at the service counter. Tips for getting a response Be clear and concise: When contacting American Airlines, clearly state your issue and provide relevant details +1 (801) 855-5905 or +1 (804) 853-9001. Be patient: You may experience wait times, especially when contacting them by phone or during peak travel seasons. Use the appropriate channel: For urgent matters, calling is typically the fastest method. For less urgent issues, consider using the online form or social media. Keep records: Save confirmation codes, ticket numbers, and any correspondence you have with American Airlines. "
IMPALA, or the Importance Weighted Actor Learner Architecture, is an off-policy actor-critic framework that decouples acting from learning and learns from experience trajectories using V-trace. Unlike the popular A3C-based agents, in which workers communicate gradients with respect to the parameters of the policy to a central parameter server, IMPALA actors communicate trajectories of experience (sequences of states, actions, and rewards) to a centralized learner. Since the learner in IMPALA has access to full trajectories of experience we use a GPU to perform updates on mini-batches of trajectories while aggressively parallelising all time independent operations. This type of decoupled architecture can achieve very high throughput. However, because the policy used to generate a trajectory can lag behind the policy on the learner by several updates at the time of gradient calculation, learning becomes off-policy. The V-trace off-policy actor-critic algorithm is used to correct for this harmful discrepancy.
Implicit Q-Learning
Neighborhood Contrastive Learning
Multimodal Fuzzy Fusion Framework
BCI MI signal Classification Framework using Fuzzy integrals. Paper: Ko, L. W., Lu, Y. C., Bustince, H., Chang, Y. C., Chang, Y., Ferandez, J., ... & Lin, C. T. (2019). Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface. IEEE Computational Intelligence Magazine, 14(1), 96-106.
Inception v2 is the second generation of Inception convolutional neural network architectures which notably uses batch normalization. Other changes include dropping dropout and removing local response normalization, due to the benefits of batch normalization.
Adaptive Dropout is a regularization technique that extends dropout by allowing the dropout probability to be different for different units. The intuition is that there may be hidden units that can individually make confident predictions for the presence or absence of an important feature or combination of features. Dropout will ignore this confidence and drop the unit out 50% of the time. Denote the activity of unit in a deep neural network by and assume that its inputs are {}. In dropout, is randomly set to zero with probability 0.5. Let be a binary variable that is used to mask, the activity , so that its value is: where is the weight from unit to unit and is the activation function and accounts for biases. Whereas in standard dropout, is Bernoulli with probability , adaptive dropout uses adaptive dropout probabilities that depends on input activities: where is the weight from unit to unit in the standout network or the adaptive dropout network; is a sigmoidal function. Here 'standout' refers to a binary belief network is that is overlaid on a neural network as part of the overall regularization technique.
Bidirectional GAN
A BiGAN, or Bidirectional GAN, is a type of generative adversarial network where the generator not only maps latent samples to generated data, but also has an inverse mapping from data to the latent representation. The motivation is to make a type of GAN that can learn rich representations for us in applications like unsupervised learning. In addition to the generator from the standard GAN framework, BiGAN includes an encoder which maps data to latent representations . The BiGAN discriminator discriminates not only in data space ( versus ), but jointly in data and latent space (tuples versus ), where the latent component is either an encoder output or a generator input .
Enhanced Sequential Inference Model
Enhanced Sequential Inference Model or ESIM is a sequential NLI model proposed in Enhanced LSTM for Natural Language Inference paper.
Squared ReLU is an activation function used in the Primer architecture in the feedforward block of the Transformer layer. It is simply squared ReLU activations. The effectiveness of higher order polynomials can also be observed in other effective Transformer nonlinearities, such as GLU variants like ReGLU and point-wise activations like approximate GELU. However, squared ReLU has drastically different asymptotics as compared to the most commonly used activation functions: ReLU, GELU and Swish. Squared ReLU does have significant overlap with ReGLU and in fact is equivalent when ReGLU’s and weight matrices are the same and squared ReLU is immediately preceded by a linear transformation with weight matrix . This leads the authors to believe that squared ReLUs capture the benefits of these GLU variants, while being simpler, without additional parameters, and delivering better quality.
An Accumulating Eligibility Trace is a type of eligibility trace where the trace increments in an accumulative way. For the memory vector :
Spatial Broadcast Decoder is an architecture that aims to improve disentangling, reconstruction accuracy, and generalization to held-out regions in data space. It provides a particularly dramatic benefit when applied to datasets with small objects. Source: Watters et al. Image source: Watters et al.
Graph InfoClust
Prime Dilated Convolution
Please enter a description about the method here
Primer is a Transformer-based architecture that improves upon the Transformer architecture with two improvements found through neural architecture search: squared RELU activations in the feedforward block, and [depthwise convolutions]() added to the attention multi-head projections: resulting in a new module called Multi-DConv-Head-Attention.
MelGAN is a non-autoregressive feed-forward convolutional architecture to perform audio waveform generation in a GAN setup. The architecture is a fully convolutional feed-forward network with mel-spectrogram as input and raw waveform as output. Since the mel-spectrogram is at a 256× lower temporal resolution, the authors use a stack of transposed convolutional layers to upsample the input sequence. Each transposed convolutional layer is followed by a stack of residual blocks with dilated convolutions. Unlike traditional GANs, the MelGAN generator does not use a global noise vector as input. To deal with 'checkerboard artifacts' in audio, instead of using PhaseShuffle, MelGAN uses kernel-size as a multiple of stride. Weight normalization is used for normalization. A window-based discriminator, similar to a PatchGAN is used for the discriminator.
TDINLINEMATH1 is a generalisation of TDINLINEMATH2 reinforcement learning algorithms, but it employs an eligibility trace and -weighted returns. The eligibility trace vector is initialized to zero at the beginning of the episode, and it is incremented on each time step by the value gradient, and then fades away by : The eligibility trace keeps track of which components of the weight vector contribute to recent state valuations. Here is the feature vector. The TD error for state-value prediction is: In TDINLINEMATH1, the weight vector is updated on each step proportional to the scalar TD error and the vector eligibility trace: Source: Sutton and Barto, Reinforcement Learning, 2nd Edition
Self-supervised Equivariant Attention Mechanism
Self-supervised Equivariant Attention Mechanism, or SEAM, is an attention mechanism for weakly supervised semantic segmentation. The SEAM applies consistency regularization on CAMs from various transformed images to provide self-supervision for network learning. To further improve the network prediction consistency, SEAM introduces the pixel correlation module (PCM), which captures context appearance information for each pixel and revises original CAMs by learned affinity attention maps. The SEAM is implemented by a siamese network with equivariant cross regularization (ECR) loss, which regularizes the original CAMs and the revised CAMs on different branches.
Skip-gram Word2Vec is an architecture for computing word embeddings. Instead of using surrounding words to predict the center word, as with CBow Word2Vec, Skip-gram Word2Vec uses the central word to predict the surrounding words. The skip-gram objective function sums the log probabilities of the surrounding words to the left and right of the target word to produce the following objective:
A R(2+1)D convolutional neural network is a network for action recognition that employs R(2+1)D convolutions in a ResNet inspired architecture. The use of these convolutions over regular 3D Convolutions reduces computational complexity, prevents overfitting, and introduces more non-linearities that allow for a better functional relationship to be modeled.
Gradient Checkpointing is a method used for reducing the memory footprint when training deep neural networks, at the cost of having a small increase in computation time.
MATE is a Transformer architecture designed to model the structure of web tables. It uses sparse attention in a way that allows heads to efficiently attend to either rows or columns in a table. Each attention head reorders the tokens by either column or row index and then applies a windowed attention mechanism. Unlike traditional self-attention, Mate scales linearly in the sequence length.
Generalized ELBO with Constrained Optimization
Contour Proposal Network
The Contour Proposal Network (CPN) detects possibly overlapping objects in an image while simultaneously fitting pixel-precise closed object contours. The CPN can incorporate state of the art object detection architectures as backbone networks into a fast single-stage instance segmentation model that can be trained end-to-end.
Multi-partition Embedding Interaction
MEI introduces the multi-partition embedding interaction technique with block term tensor format to systematically address the efficiency--expressiveness trade-off in knowledge graph embedding. It divides the embedding vector into multiple partitions and learns the local interaction patterns from data instead of using fixed special patterns as in ComplEx or SimplE models. This enables MEI to achieve optimal efficiency--expressiveness trade-off, not just being fully expressive. Previous methods such as TuckER, RESCAL, DistMult, ComplEx, and SimplE are suboptimal restricted special cases of MEI.
ShuffleNet V2 Downsampling Block is a block for spatial downsampling used in the ShuffleNet V2 architecture. Unlike the regular ShuffleNet V2 block, the channel split operator is removed so the number of output channels is doubled.
Please enter a description about the method here
Inception-A is an image model block used in the Inception-v4 architecture.
Differentiable Digital Signal Processing