5,489 machine learning methods and techniques
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
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1cycle learning rate scheduling policy
Automatic Search for Parsimonious Models
The principle of parsimony, also known as Occam's razor, elucidates the preference for the simplest explanation that provides optimal results when faced with multiple options. Thus, we can assert that the principle of parsimony is justified by "the assumption that is both the simplest and contains all the necessary information required to comprehend the experiment at hand." This principle finds application in various scenarios or events in our daily lives, including predictions in Data Science models. It is widely recognized that a less complex model will produce more stable predictions, exhibit greater resilience to noise and disturbances, and be more manageable for maintenance and analysis. Additionally, reducing the number of features can lead to further cost savings by diminishing the use of sensors, lowering energy consumption, minimizing information acquisition costs, reducing maintenance requirements, and mitigating the necessity to retrain models due to feature fluctuations caused by noise, outliers, data drift, etc. The concurrent optimization of hyperparameters (HO) and feature selection (FS) for achieving Parsimonious Model Selection (PMS) is an ongoing area of active research. Nonetheless, the effective selection of appropriate hyperparameters and feature subsets presents a challenging combinatorial problem, frequently requiring the application of efficient heuristic methods.
Latent Optimisation is a technique used for generative adversarial networks to refine the sample quality of . Specifically, it exploits knowledge from the discriminator to refine the latent source . Intuitively, the gradient points in the direction that better satisfies the discriminator , which implies better samples. Therefore, instead of using the randomly sampled , we uses the optimised latent: Source: LOGAN .
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SwiGLU is an activation function which is a variant of GLU. The definition is as follows:
Matrix-power Normalization
Characteristic Function Estimation for Discrete Probability Distributions
Forward gradients are unbiased estimators of the gradient for a function , given by . Here is a random vector, which must satisfy the following conditions in order for to be an unbiased estimator of for all for all for all Forward gradients can be computed with a single jvp (Jacobian Vector Product), which enables the use of the forward mode of autodifferentiation instead of the usual reverse mode, which has worse computational characteristics.
LayerScale is a method used for vision transformer architectures to help improve training dynamics. It adds a learnable diagonal matrix on output of each residual block, initialized close to (but not at) 0. Adding this simple layer after each residual block improves the training dynamic, allowing for the training of deeper high-capacity image transformers that benefit from depth. Specifically, LayerScale is a per-channel multiplication of the vector produced by each residual block, as opposed to a single scalar, see Figure (d). The objective is to group the updates of the weights associated with the same output channel. Formally, LayerScale is a multiplication by a diagonal matrix on output of each residual block. In other words: where the parameters and are learnable weights. The diagonal values are all initialized to a fixed small value we set it to until depth 18 , for depth 24 and for deeper networks. This formula is akin to other normalization strategies ActNorm or LayerNorm but executed on output of the residual block. Yet LayerScale seeks a different effect: ActNorm is a data-dependent initialization that calibrates activations so that they have zero-mean and unit variance, like BatchNorm. In contrast, in LayerScale, we initialize the diagonal with small values so that the initial contribution of the residual branches to the function implemented by the transformer is small. In that respect the motivation is therefore closer to that of ReZero, SkipInit, Fixup and T-Fixup: to train closer to the identity function and let the network integrate the additional parameters progressively during the training. LayerScale offers more diversity in the optimization than just adjusting the whole layer by a single learnable scalar as in ReZero/SkipInit, Fixup and T-Fixup.
Hunger Games Search
Hunger Games Search (HGS) is a general-purpose population-based optimization technique with a simple structure, special stability features and very competitive performance to realize the solutions of both constrained and unconstrained problems more effectively. HGS is designed according to the hunger-driven activities and behavioural choice of animals. This dynamic, fitness-wise search method follows a simple concept of “Hunger” as the most crucial homeostatic motivation and reason for behaviours, decisions, and actions in the life of all animals to make the process of optimization more understandable and consistent for new users and decision-makers. The Hunger Games Search incorporates the concept of hunger into the feature process; in other words, an adaptive weight based on the concept of hunger is designed and employed to simulate the effect of hunger on each search step. It follows the computationally logical rules (games) utilized by almost all animals and these rival activities and games are often adaptive evolutionary by securing higher chances of survival and food acquisition. This method's main feature is its dynamic nature, simple structure, and high performance in terms of convergence and acceptable quality of solutions, proving to be more efficient than the current optimization methods. Implementation of the HGS algorithm is available at https://aliasgharheidari.com/HGS.html.
Auxiliary Batch Normalization is a type of regularization used in adversarial training schemes. The idea is that adversarial examples should have a separate batch normalization components to the clean examples, as they have different underlying statistics.
Softsign is an activation function for neural networks: Image Source: Sefik Ilkin Serengil
A Global Context Block is an image model block for global context modeling. The aim is to have both the benefits of the simplified non-local block with effective modeling of long-range dependencies, and the squeeze-excitation block with lightweight computation. In the Global Context framework, we have (a) global attention pooling, which adopts a 1x1 convolution and softmax function to obtain the attention weights, and then performs the attention pooling to obtain the global context features, (b) feature transform via a 1x1 convolution ; (c) feature aggregation, which employs addition to aggregate the global context features to the features of each position. Taken as a whole, the GC block is proposed as a lightweight way to achieve global context modeling.
ReLIC, or Representation Learning via Invariant Causal Mechanisms, is a self-supervised learning objective that enforces invariant prediction of proxy targets across augmentations through an invariance regularizer which yields improved generalization guarantees. We can write the objective as: where is the proxy task loss and is the Kullback-Leibler (KL) divergence. Note that any distance measure on distributions can be used in place of the KL divergence. Concretely, as proxy task we associate to every datapoint the label . This corresponds to the instance discrimination task, commonly used in contrastive learning. We take pairs of points to compute similarity scores and use pairs of augmentations to perform a style intervention. Given a batch of samples , we use with data augmented with and a softmax temperature parameter. We encode using a neural network and choose to be related to , e.g. or as a network with an exponential moving average of the weights of (e.g. target networks). To compare representations we use the function where is a fully-connected neural network often called the critic. Combining these pieces, we learn representations by minimizing the following objective over the full set of data and augmentations with the number of points we use to construct the contrast set and the weighting of the invariance penalty. The shorthand is used for . The Figure shows a schematic of the RELIC objective.
Crossmodal Contrastive Learning
CMCL, or Crossmodal Contrastive Learning, is a method for unifying visual and textual representations into the same semantic space based on a large-scale corpus of image collections, text corpus and image-text pairs. The CMCL aligns the visual representations and textual representations, and unifies them into the same semantic space based on image-text pairs. As shown in the Figure, to facilitate different levels of semantic alignment between vision and language, a series of text rewriting techniques are utilized to improve the diversity of cross-modal information. Specifically, for an image-text pair, various positive examples and hard negative examples can be obtained by rewriting the original caption at different levels. Moreover, to incorporate more background information from the single-modal data, text and image retrieval are also applied to augment each image-text pair with various related texts and images. The positive pairs, negative pairs, related images and texts are learned jointly by CMCL. In this way, the model can effectively unify different levels of visual and textual representations into the same semantic space, and incorporate more single-modal knowledge to enhance each other.
FBNet Block is an image model block used in the FBNet architectures discovered through DNAS neural architecture search. The basic building blocks employed are depthwise convolutions and a residual connection.
Weight Standardization is a normalization technique that smooths the loss landscape by standardizing the weights in convolutional layers. Different from the previous normalization methods that focus on activations, WS considers the smoothing effects of weights more than just length-direction decoupling. Theoretically, WS reduces the Lipschitz constants of the loss and the gradients. Hence, WS smooths the loss landscape and improves training. In Weight Standardization, instead of directly optimizing the loss on the original weights , we reparameterize the weights as a function of , i.e. , and optimize the loss on by SGD: where Similar to Batch Normalization, WS controls the first and second moments of the weights of each output channel individually in convolutional layers. Note that many initialization methods also initialize the weights in some similar ways. Different from those methods, WS standardizes the weights in a differentiable way which aims to normalize gradients during back-propagation. Note that we do not have any affine transformation on . This is because we assume that normalization layers such as BN or GN will normalize this convolutional layer again.
Network Dissection is an interpretability method for CNNs that evaluates the alignment between individual hidden units and a set of visual semantic concepts. By identifying the best alignments, units are given human interpretable labels across a range of objects, parts, scenes, textures, materials, and colors. The measurement of interpretability proceeds in three steps: - Identify a broad set of human-labeled visual concepts. - Gather the response of the hidden variables to known concepts. - Quantify alignment of hidden variable−concept pairs.
Bilinear Attention
Bi-attention employs the attention-in-attention (AiA) mechanism to capture second-order statistical information: the outer point-wise channel attention vectors are computed from the output of the inner channel attention.
online deep learning
Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is significantly more challenging since the optimization of the DNN objective function is non-convex, and regular backpropagation does not work well in practice, especially for online learning settings.
Relation-aware Global Attention
In relation-aware global attention (RGA) stresses the importance of global structural information provided by pairwise relations, and uses it to produce attention maps. RGA comes in two forms, spatial RGA (RGA-S) and channel RGA (RGA-C). RGA-S first reshapes the input feature map to and the pairwise relation matrix is computed using \begin{align} Q &= \delta(W^QX) \end{align} \begin{align} K &= \delta(W^KX) \end{align} \begin{align} R &= Q^TK \end{align} The relation vector at position is defined by stacking pairwise relations at all positions: \begin{align} ri = [R(i, :); R(:,i)] \end{align} and the spatial relation-aware feature can be written as \begin{align} Yi = [g^c\text{avg}(\delta(W^\varphi xi)); \delta(W^\phi ri)] \end{align} where denotes global average pooling in the channel domain. Finally, the spatial attention score at position is given by \begin{align} ai = \sigma(W2\delta(W1yi)) \end{align} RGA-C has the same form as RGA-S, except for taking the input feature map as a set of -dimensional features. RGA uses global relations to generate the attention score for each feature node, so provides valuable structural information and significantly enhances the representational power. RGA-S and RGA-C are flexible enough to be used in any CNN network; Zhang et al. propose using them jointly in sequence to better capture both spatial and cross-channel relationships.
Triplet attention comprises of three branches each responsible for capturing crossdimension between the spatial dimensions and channel dimension of the input. Given an input tensor with shape (C × H × W), each branch is responsible for aggregating cross-dimensional interactive features between either the spatial dimension H or W and the channel dimension C.
MODEL EDITOR NETWORKS WITH GRADIENT DECOMPOSITION
Model-based Subsampling
To avoid the problem caused by low-frequent entity-relation pairs, our MBS uses the estimated probabilities from a trained model to calculate frequencies for each triplet and query. By using , the NS loss in KGE with MBS is represented as follows: \begin{align} &\ell{mbs}(\mathbf{\theta};\mathbf{\theta}') \nonumber \\ =&-\frac{1}{|D|}\sum{(x,y) \in D} \Bigl[A{mbs}(\mathbf{\theta}')\log(\sigma(s{\mathbf{\theta}}(x,y)+\gamma))\nonumber\\ &+\frac{1}{\nu}sum{y{i}\sim pn(y{i}|x)}^{\nu}B{mbs}(\mathbf{\theta}')\log(\sigma(-s{\mathbf{\theta}}(x,yi)-\gamma))\Bigr], \end{align}
DeepCluster is a self-supervision approach for learning image representations. DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update the weights of the network
Domain Adaptative Neighborhood Clustering via Entropy Optimization
Domain Adaptive Neighborhood Clustering via Entropy Optimization (DANCE) is a self-supervised clustering method that harnesses the cluster structure of the target domain using self-supervision. This is done with a neighborhood clustering technique that self-supervises feature learning in the target. At the same time, useful source features and class boundaries are preserved and adapted with a partial domain alignment loss that the authors refer to as entropy separation loss. This loss allows the model to either match each target example with the source, or reject it as unknown.
Polynomial Rate Decay is a learning rate schedule where we polynomially decay the learning rate.
A Bottleneck Transformer Block is a block used in Bottleneck Transformers that replaces the spatial 3 × 3 convolution layer in a Residual Block with Multi-Head Self-Attention (MHSA).
A Scatter Connection is a type of connection that allows a vector to be "scattered" onto a layer representing a map, so that a vector at a specific location corresponds to objects of interest at that location (e.g. units in Starcraft II). This allows for the integration of spatial and non-spatial features.
Metropolis-Hastings is a Markov Chain Monte Carlo (MCMC) algorithm for approximate inference. It allows for sampling from a probability distribution where direct sampling is difficult - usually owing to the presence of an intractable integral. M-H consists of a proposal distribution to draw a parameter value. To decide whether is accepted or rejected, we then calculate a ratio: We then draw a random number and accept if it is under the ratio, reject otherwise. If we accept, we set and repeat. By the end we have a sample of values that we can use to form quantities over an approximate posterior, such as the expectation and uncertainty bounds. In practice, we typically have a period of tuning to achieve an acceptable acceptance ratio for the algorithm, as well as a warmup period to reduce bias towards initialization values. Image: Samuel Hudec
First Integer Neighbor Clustering Hierarchy
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Self-Supervised Deep Supervision
The method exploits the finding that high correlation of segmentation performance among each U-Net's decoder layer -- with discriminative layer attached -- tends to have higher segmentation performance in the final segmentation map. By introducing an "Inter-layer Divergence Loss", based on Kulback-Liebler Divergence, to promotes the consistency between each discriminative output from decoder layers by minimizing the divergence. If we assume that each decoder layer is equivalent to PDE function parameterized by weight parameter : Then our objective is trying to make each discriminative output similar to each other: Hence the objective is to .
Dual Attention Network
In the field of scene segmentation, encoder-decoder structures cannot make use of the global relationships between objects, whereas RNN-based structures heavily rely on the output of the long-term memorization. To address the above problems, Fu et al. proposed a novel framework, the dual attention network (DANet), for natural scene image segmentation. Unlike CBAM and BAM, it adopts a self-attention mechanism instead of simply stacking convolutions to compute the spatial attention map, which enables the network to capture global information directly. DANet uses in parallel a position attention module and a channel attention module to capture feature dependencies in spatial and channel domains. Given the input feature map , convolution layers are applied first in the position attention module to obtain new feature maps. Then the position attention module selectively aggregates the features at each position using a weighted sum of features at all positions, where the weights are determined by feature similarity between corresponding pairs of positions. The channel attention module has a similar form except for dimensional reduction to model cross-channel relations. Finally the outputs from the two branches are fused to obtain final feature representations. For simplicity, we reshape the feature map to whereupon the overall process can be written as \begin{align} Q,\quad K,\quad V &= WqX,\quad WkX,\quad WvX \end{align} \begin{align} Y^\text{pos} &= X+ V\text{Softmax}(Q^TK) \end{align} \begin{align} Y^\text{chn} &= X+ \text{Softmax}(XX^T)X \end{align} \begin{align} Y &= Y^\text{pos} + Y^\text{chn} \end{align} where , , are used to generate new feature maps. The position attention module enables DANet to capture long-range contextual information and adaptively integrate similar features at any scale from a global viewpoint, while the channel attention module is responsible for enhancing useful channels as well as suppressing noise. Taking spatial and channel relationships into consideration explicitly improves the feature representation for scene segmentation. However, it is computationally costly, especially for large input feature maps.
Factor Graph Attention
A general multimodal attention unit for any number of modalities. Graphical models inspire it, i.e., it infers several attention beliefs via aggregated interaction messages.
SAINT is a hybrid deep learning approach to solving tabular data problems. SAINT performs attention over both rows and columns, and it includes an enhanced embedding method. The architecture, pre-training and training pipeline are as follows: - layers with 2 attention blocks each, one self-attention block, and a novel intersample attention blocks that computes attention across samples are used. - For pre-training, this involves minimizing the contrastive and denoising losses between a given data point and its views generated by CutMix and mixup. During finetuning/regular training, data passes through an embedding layer and then the SAINT model. Lastly, the contextual embeddings from SAINT are used to pass only the embedding corresponding to the CLS token through an MLP to obtain the final prediction.
Kernel Activation Function
A Kernel Activation Function is a non-parametric activation function defined as a one-dimensional kernel approximator: where: 1. The dictionary of the kernel elements is fixed by sampling the -axis with a uniform step around 0. 2. The user selects the kernel function (e.g., Gaussian, ReLU, Softplus) and the number of kernel elements as a hyper-parameter. A larger dictionary leads to more expressive activation functions and a larger number of trainable parameters. 3. The linear coefficients are adapted independently at every neuron via standard back-propagation. In addition, the linear coefficients can be initialized using kernel ridge regression to behave similarly to a known function in the beginning of the optimization process.
Overfitting Conditional Diffusion Model
Collaborative Distillation is a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the number of convolutional filters. The main idea is underpinned by a finding that the encoder-decoder pairs construct an exclusive collaborative relationship, which is regarded as a new kind of knowledge for style transfer models.
Dense Contrastive Learning is a self-supervised learning method for dense prediction tasks. It implements self-supervised learning by optimizing a pairwise contrastive (dis)similarity loss at the pixel level between two views of input images. Contrasting with regular contrastive loss, the contrastive loss is computed between the single feature vectors outputted by the global projection head, at the level of global feature, while the dense contrastive loss is computed between the dense feature vectors outputted by the dense projection head, at the level of local feature.
Excess of Mass
Excess of Mass aim to maximized the cluster stability
Sinusoidal Representation Network
Siren, or Sinusoidal Representation Network, is a periodic activation function for implicit neural representations. Specifically it uses the sine as a periodic activation function:
Global-and-Local attention
Most attention mechanisms learn where to focus using only weak supervisory signals from class labels, which inspired Linsley et al. to investigate how explicit human supervision can affect the performance and interpretability of attention models. As a proof of concept, Linsley et al. proposed the global-and-local attention (GALA) module, which extends an SE block with a spatial attention mechanism. Given the input feature map , GALA uses an attention mask that combines global and local attention to tell the network where and on what to focus. As in SE blocks, global attention aggregates global information by global average pooling and then produces a channel-wise attention weight vector using a multilayer perceptron. In local attention, two consecutive convolutions are conducted on the input to produce a positional weight map. The outputs of the local and global pathways are combined by addition and multiplication. Formally, GALA can be represented as: \begin{align} sg &= W{2} \delta (W{1}\text{GAP}(x)) \end{align} \begin{align} sl &= Conv2^{1\times 1} (\delta(Conv1^{1\times1}(X))) \end{align} \begin{align} sg^ &= \text{Expand}(sg) \end{align} \begin{align} sl^ &= \text{Expand}(sl) \end{align} \begin{align} s &= \tanh(a(sg^\ + sl^\) +m \cdot (sg^\ sl^\) ) \end{align} \begin{align} Y &= sX \end{align} where are learnable parameters representing channel-wise weight vectors. Supervised by human-provided feature importance maps, GALA has significantly improved representational power and can be combined with any CNN backbone.
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Amplifying Sine Unit: An Oscillatory Activation Function for Deep Neural Networks to Recover Nonlinear Oscillations Efficiently
2023
Euclidean Norm Regularization is a regularization step used in generative adversarial networks, and is typically added to both the generator and discriminator losses: where the scalar weight is a parameter. Image: LOGAN
Zero Redundancy Optimizer (ZeRO) is a sharded data parallel method for distributed training. ZeRODP removes the memory state redundancies across data-parallel processes by partitioning the model states instead of replicating them, and it retains the compute/communication efficiency by retaining the computational granularity and communication volume of DP using a dynamic communication schedule during training.