8,725 machine learning methods and techniques
Base boosting is a generalization of gradient boosting, which fits a hybrid additive and varying coefficient model. - Namely, gradient boosting fits an additive model: \begin{equation} h(X ; \{ \alpha, \theta\}) = \alpha{0} + \sum{k=1}^{K} \alpha{k} b(X ; \theta{k}), \end{equation} where the boosting mechanism begins optimization in function space at a constant model. - In contrast, base boosting fits the hybrid additive and varying coefficient model: \begin{equation} h(X ; \{ \alpha, \theta\}) = \gamma(X) + \sum{k=1}^{K} \alpha{k} b(X ; \theta{k}), \end{equation} where the boosting mechanism begins optimization in function space at a base model, which may be a non-constant model. A special case is the coordinate functional: \begin{equation} \gamma(X) = \pi{j}(X) = X{j} \end{equation} where denotes a prediction generated by the base model. - This setup facilitates knowledge transfer between the base model and boosting mechanism.
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ReGLU is an activation function which is a variant of GLU. The definition is as follows:
A HaloNet is a self-attention based model for efficient image classification. It relies on a local self-attention architecture that efficiently maps to existing hardware with haloing. The formulation breaks translational equivariance, but the authors observe that it improves throughput and accuracies over the centered local self-attention used in regular self-attention. The approach also utilises a strided self-attentive downsampling operation for multi-scale feature extraction.
Watch Your Step
RPDet, or RepPoints Detector, is a anchor-free, two-stage object detection model based on deformable convolutions. RepPoints serve as the basic object representation throughout the detection system. Starting from the center points, the first set of RepPoints is obtained via regressing offsets over the center points. The learning of these RepPoints is driven by two objectives: 1) the top-left and bottom-right points distance loss between the induced pseudo box and the ground-truth bounding box; 2) the object recognition loss of the subsequent stage.
Adaptive Locally Connected Neuron
The Adaptive Locally Connected Neuron (ALCN) is a topology aware, and locally adaptive -focusing neuron:
Graph Finite-State Automaton
Graph Finite-State Automaton, or GFSA, is a differentiable layer for learning graph structure that adds a new edge type (expressed as a weighted adjacency matrix) to a base graph. This layer can be trained end-to-end to add derived relationships (edges) to arbitrary graph-structured data based on performance on a downstream task.
Sparse Sinkhorn Attention is an attention mechanism that reduces the memory complexity of the dot-product attention mechanism and is capable of learning sparse attention outputs. It is based on the idea of differentiable sorting of internal representations within the self-attention module. SSA incorporates a meta sorting network that learns to rearrange and sort input sequences. Sinkhorn normalization is used to normalize the rows and columns of the sorting matrix. The actual SSA attention mechanism then acts on the block sorted sequences.
DBlock is a residual based block used in the discriminator of the GAN-TTS architecture. They are similar to the GBlocks used in the generator, but without batch normalisation.
T-Fixup is an initialization method for Transformers that aims to remove the need for layer normalization and warmup. The initialization procedure is as follows: - Apply Xavier initialization for all parameters excluding input embeddings. Use Gaussian initialization for input embeddings where is the embedding dimension. - Scale and matrices in each decoder attention block, weight matrices in each decoder MLP block and input embeddings and in encoder and decoder by - Scale and matrices in each encoder attention block and weight matrices in each encoder MLP block by
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Massively multilingual probing based on Universal Dependencies
ARM-Net is an adaptive relation modeling network tailored for structured data, and a lightweight framework ARMOR based on ARM-Net for relational data analytics. The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature. The authors propose a novel sparse attention mechanism to dynamically generate the interaction weights given the input tuple, so that we can explicitly model cross features of arbitrary orders with noisy features filtered selectively. Then during model inference, ARM-Net can specify the cross features being used for each prediction for higher accuracy and better interpretability.
Video Language Graph Matching Network
VLG-Net leverages recent advantages in Graph Neural Networks (GCNs) and leverages a novel multi-modality graph-based fusion method for the task of natural language video grounding.
Directional Sparse FIltering
Adversarial-Learned Loss for Domain Adaptation is a method for domain adaptation that combines adversarial learning with self-training. Specifically, the domain discriminator has to produce different corrected labels for different domains, while the feature generator aims to confuse the domain discriminator. The adversarial process finally leads to a proper confusion matrix on the target domain. In this way, ALDA takes the strengths of domain-adversarial learning and self-training based methods.
bilayer convolutional neural network
Poly-CAM
Residual Bidirectional Long Short-Term Memory
Please enter a description about the method here
DNN2LR is an automatic feature crossing method to find feature interactions in a deep neural network, and use them as cross features in logistic regression. In general, DNN2LR consists of two steps: (1) generating a compact and accurate candidate set of cross feature fields; (2) searching in the candidate set for the final cross feature fields.
A variant of CutMix which randomly samples masks from Fourier space.
Shape Adaptor is a novel resizing module for neural networks. It is a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided convolution. This module allows for a learnable shaping factor which differs from the traditional resizing layers that are fixed and deterministic. Image Source: Liu et al.
NoisyNet-DQN is a modification of a DQN that utilises noisy linear layers for exploration instead of -greedy exploration as in the original DQN formulation.
BiSeNet V2 is a two-pathway architecture for real-time semantic segmentation. One pathway is designed to capture the spatial details with wide channels and shallow layers, called Detail Branch. In contrast, the other pathway is introduced to extract the categorical semantics with narrow channels and deep layers, called Semantic Branch. The Semantic Branch simply requires a large receptive field to capture semantic context, while the detail information can be supplied by the Detail Branch. Therefore, the Semantic Branch can be made very lightweight with fewer channels and a fast-downsampling strategy. Both types of feature representation are merged to construct a stronger and more comprehensive feature representation.
ZeRO-Infinity is a sharded data parallel system that extends ZeRO with new innovations in heterogeneous memory access called the infinity offload engine. This allows ZeRO-Infinity to support massive model sizes on limited GPU resources by exploiting CPU and NVMe memory simultaneously. In addition, ZeRO-Infinity also introduces a novel GPU memory optimization technique called memory-centric tiling to support extremely large individual layers that would otherwise not fit in GPU memory even one layer at a time.
Automatic Structured Variational Inference
Automatic Structured Variational Inference (ASVI) is a fully automated method for constructing structured variational families, inspired by the closed-form update in conjugate Bayesian models. These convex-update families incorporate the forward pass of the input probabilistic program and can therefore capture complex statistical dependencies. Convex-update families have the same space and time complexity as the input probabilistic program and are therefore tractable for a very large family of models including both continuous and discrete variables.
LeVIT is a hybrid neural network for fast inference image classification. LeViT is a stack of transformer blocks, with pooling steps to reduce the resolution of the activation maps as in classical convolutional architectures. This replaces the uniform structure of a Transformer by a pyramid with pooling, similar to the LeNet architecture
Contour Stochastic Gradient Langevin Dynamics
Simulations of multi-modal distributions can be very costly and often lead to unreliable predictions. To accelerate the computations, we propose to sample from a flattened distribution to accelerate the computations and estimate the importance weights between the original distribution and the flattened distribution to ensure the correctness of the distribution.
NAS-FCOS consists of two sub networks, an FPN and a set of prediction heads which have shared structures. One notable difference with other FPN-based one-stage detectors is that our heads have partially shared weights. Only the last several layers of the predictions heads (marked as yellow) are tied by their weights. The number of layers to share is decided automatically by the search algorithm. Note that both FPN and head are in our actual search space; and have more layers than shown in this figure.
Anycost GAN is a type of generative adversarial network for image synthesis and editing. Given an input image, we project it into the latent space with encoder and backward optimization. We can modify the latent code with user input to edit the image. During editing, a sub-generator of small cost is used for fast and interactive preview; during idle time, the full cost generator renders the final, high-quality output. The outputs from the full and sub-generators are visually consistent during projection and editing.
GAN-TTS is a generative adversarial network for text-to-speech synthesis. The architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyze the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. The generator architecture consists of several GBlocks, which are residual based (dilated) convolution blocks. GBlocks 3–7 gradually upsample the temporal dimension of hidden representations by factors of 2, 2, 2, 3, 5, while the number of channels is reduced by GBlocks 3, 6 and 7 (by a factor of 2 each). The final convolutional layer with Tanh activation produces a single-channel audio waveform. Instead of a single discriminator, GAN-TTS uses an ensemble of Random Window Discriminators (RWDs) which operate on randomly sub-sampled fragments of the real or generated samples. The ensemble allows for the evaluation of audio in different complementary ways.
LMOT: Efficient Light-Weight Detection and Tracking in Crowds
Rana Mostafa, Hoda Baraka and AbdelMoniem Bayoumi LMOT, i.e., Light-weight Multi-Object Tracker, performs joint pedestrian detection and tracking. LMOT introduces a simplified DLA-34 encoder network to extract detection features for the current image that are computationally efficient. Furthermore, we generate efficient tracking features using a linear transformer for the prior image frame and its corresponding detection heatmap. After that, LMOT fuses both detection and tracking feature maps in a multi-layer scheme and performs a two-stage online data association relying on the Kalman filter to generate tracklets. We evaluated our model on the challenging real-world MOT16/17/20 datasets, showing LMOT significantly outperforms the state-of-the-art trackers concerning runtime while maintaining high robustness. LMOT is approximately ten times faster than state-of-the-art trackers while being only 3.8% behind in performance accuracy on average leading to a much computationally lighter model. Code: https://github.com/RanaMostafaAbdElMohsen/LMOT Paper: https://doi.org/10.1109/ACCESS.2022.3197157
Deflation is a video-to-image operation to transform a video network into a network that can ingest a single image. In the two types of video networks considered in the original paper, this deflation corresponds to the following operations: for 3D convolutional based networks, summing the 3D spatio-temporal filters over the temporal dimension to obtain 2D filters; for TSM networks,, turning off the channel shifting which results in a standard residual architecture (ResNet50) for images.
Generic RoI Extractor
GroIE is an RoI extractor which intends to overcome the limitation of existing extractors which select only one (the best) layer from the FPN. The intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer introduces non-local building blocks and attention mechanisms to boost the performance.
Multiple Random Window Discriminator is a discriminator used for the GAN-TTS text-to-speech architecture. These discriminators operate on randomly sub-sampled fragments of the real or generated samples. The ensemble allows for the evaluation of audio in different complementary ways, and is obtained by taking a Cartesian product of two parameter spaces: (i) the size of the random windows fed into the discriminator; (ii) whether a discriminator is conditioned on linguistic and pitch features. For example, in the authors' best-performing model, they consider five window sizes (240, 480, 960, 1920, 3600 samples), which yields 10 discriminators in total. Using random windows of different size, rather than the full generated sample, has a data augmentation effect and also reduces the computational complexity of RWDs. In the first layer of each discriminator, the MRWD reshapes (downsamples) the input raw waveform to a constant temporal dimension by moving consecutive blocks of samples into the channel dimension, i.e. from to , where is the downsampling factor (e.g. for input window size ). This way, all the RWDs have the same architecture and similar computational complexity despite different window sizes. The conditional discriminators have access to linguistic and pitch features, and can measure whether the generated audio matches the input conditioning. This means that random windows in conditional discriminators need to be aligned with the conditioning frequency to preserve the correspondence between the waveform and linguistic features within the sampled window. This limits the valid sampling to that of the frequency of the conditioning signal (200Hz, or every 5ms). The unconditional discriminators, on the contrary, only evaluate whether the generated audio sounds realistic regardless of the conditioning. The random windows for these discriminators are sampled without constraints at full 24kHz frequency, which further increases the amount of training data. For the architecture, the discriminators consists of blocks (DBlocks) that are similar to the GBlocks used in the generator, but without batch normalisation. Unconditional RWDs are composed entirely of DBlocks. In conditional RWDs, the input waveform is gradually downsampled by DBlocks, until the temporal dimension of the activation is equal to that of the conditioning, at which point a conditional DBlock is used. This joint information is then passed to the remaining DBlocks, whose final output is average-pooled to obtain a scalar. The dilation factors in the DBlocks’ convolutions follow the pattern 1, 2, 1, 2 – unlike the generator, the discriminator operates on a relatively small window, and the authors did not observe any benefit from using larger dilation factors.
PREDATOR is a model for pairwise point-cloud registration with deep attention to the overlap region. Its key novelty is an overlap-attention block for early information exchange between the latent encodings of the two point clouds. In this way the subsequent decoding of the latent representations into per-point features is conditioned on the respective other point cloud, and thus can predict which points are not only salient, but also lie in the overlap region between the two point clouds.
Second-order Clipped Stochastic Optimization
Please enter a description about the method here
DenseNet-Elastic is a convolutional neural network that is a modification of a DenseNet with elastic blocks (extra upsampling and downsampling).
Filter Response Normalization (FRN) is a type of normalization that combines normalization and an activation function, which can be used as a replacement for other normalizations and activations. It operates on each activation channel of each batch element independently, eliminating the dependency on other batch elements. To demonstrate, assume we are dealing with the feed-forward convolutional neural network. We follow the usual convention that the filter responses (activation maps) produced after a convolution operation are a 4D tensor with shape , where is the mini-batch size, are the spatial extents of the map, and is the number of filters used in convolution. is also referred to as output channels. Let , where , be the vector of filter responses for the filter for the batch point. Let , be the mean squared norm of . Then Filter Response Normalization is defined as the following: where is a small positive constant to prevent division by zero. A lack of mean centering in FRN can lead to activations having an arbitrary bias away from zero. Such a bias in conjunction with ReLU can have a detrimental effect on learning and lead to poor performance and dead units. To address this the authors augment ReLU with a learned threshold to yield: Since , the effect of this activation is the same as having a shared bias before and after ReLU.
CodeSLAM represents the 3D geometry of a scene using the latent space of a variational autoencoder. The depth thus becomes a function of the RGB image and the unknown code, . During training time, the weights of the network are learnt by training the generator and encoder using a standard autoencoding task. At test time the code and the pose of the images is found by optimizing the reprojection error over multiple images.
Conditional Position Encoding Vision Transformer
CPVT, or Conditional Position Encoding Vision Transformer, is a type of vision transformer which utilizes conditional positional encoding. Other than the new encodings, it follows the same architecture of ViT and DeiT.
Energy Based Processes extend energy based models to exchangeable data while allowing neural network parameterizations of the energy function. They extend the previously separate stochastic process and latent variable model perspectives in a common framework. The result is a generalization of Gaussian processes and Student-t processes that exploits EBMs for greater flexibility.
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Domain-Symmetric Network
Domain-Symmetric Network, or SymmNet, is an algorithm for unsupervised multi-class domain adaptation. It features an adversarial strategy of domain confusion and discrimination.
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