5,489 machine learning methods and techniques
G3D is a unified spatial-temporal graph convolutional operator that directly models cross-spacetime joint dependencies. It leverages dense cross-spacetime edges as skip connections for direct information propagation across the 3D spatial-temporal graph.
First Integer Neighbor Clustering Hierarchy (FINCH))
FINCH is a parameter-free fast and scalable clustering algorithm. it stands out for its speed and clustering quality.
Deformable Convolutional Networks
Deformable ConvNets do not learn an affine transformation. They divide convolution into two steps, firstly sampling features on a regular grid from the input feature map, then aggregating sampled features by weighted summation using a convolution kernel. The process can be written as: \begin{align} Y(p{0}) &= \sum{pi \in \mathcal{R}} w(p{i}) X(p{0} + p{i}) \end{align} \begin{align} \mathcal{R} &= \{(-1,-1), (-1, 0), \dots, (1, 1)\} \end{align} The deformable convolution augments the sampling process by introducing a group of learnable offsets which can be generated by a lightweight CNN. Using the offsets , the deformable convolution can be formulated as: \begin{align} Y(p{0}) &= \sum{pi \in \mathcal{R}} w(p{i}) X(p{0} + p{i} + \Delta p{i}). \end{align} Through the above method, adaptive sampling is achieved. However, is a floating point value unsuited to grid sampling. To address this problem, bilinear interpolation is used. Deformable RoI pooling is also used, which greatly improves object detection. Deformable ConvNets adaptively select the important regions and enlarge the valid receptive field of convolutional neural networks; this is important in object detection and semantic segmentation tasks.
Adaptive Moment Estimation - Mini
Adam-mini is a memory-efficient Adam variant that achieves on-par or better performance than AdamW with 45% to 50% less memory footprint. Adam-mini reduces the memory footprint by cutting down the learning rate resources in Adam (i.e., ). The authors find that ≥ 90% of these learning rates in could be harmlessly removed if they (1) carefully partition the parameters into blocks following their proposed principle on Hessian structure; (2) assign a single but good learning rate to each parameter block. They further find that, for each of these parameter blocks, there exists a single high-quality learning rate that can outperform Adam, provided that sufficient resources are available to search it out.
Smooth ReLU
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Our proposed loss function is a combination of BCE Loss, Focal Loss, and Dice loss. Each one of them contributes individually to improve performance further details of loss functions are mentioned below, (1) BCE Loss calculates probabilities and compares each actual class output with predicted probabilities which can be either 0 or 1, it is based on Bernoulli distribution loss, it is mostly used when there are only two classes are available in our case there are exactly two classes are available one is background and other is foreground. In a proposed method it is used for pixel-level classification. (2) Focal Loss is a variant of BCE, it enables the model to focus on learning hard examples by decreasing the wights of easy examples it works well when the data is highly imbalanced. (3) Dice Loss is inspired by the Dice Coefficient Score which is an evaluation metric used to evaluate the results of image segmentation tasks. Dice Coefficient is convex in nature so it has been changed, so it can be more traceable. It is used to calculate the similarity between two images, Dice Loss represent as We proposed a Loss function which is a combination of all three above mention loss functions to benefit from all, BCE is used for pixel-wise classification, Focal Loss is used for learning hard examples, we use 0.25 as the value for alpha and 2.0 as the value of gamma. Dice Loss is used for learning better boundary representation, our proposed loss function represent as \begin{equation} Loss = \left( BCE Loss + Focal Loss \right) + Dice Loss \end{equation}
Multi-modal Teacher for Masked Modality Learning
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Continual Learning through Adjustment Suppression and Sparsity Promotion
Conditional 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 normalization. Unlike the DBlock, the Conditional DBlock adds the embedding of the linguistic features after the first convolution.
Adaptive Smooth Optimizer
AdaSmooth is a stochastic optimization technique that allows for per-dimension learning rate method for SGD. It is an extension of Adagrad and AdaDelta that seek 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 while AdaSmooth adaptively selects the size of the window. Given the window size , the effective ratio is calculated by Given the effective ratio, the scaled smoothing constant is obtained by: The running average at time step then depends only on the previous average and current gradient: Usually is set to around and is set to around 0.99. The update step the follows: which is incorporated into the final update: The main advantage of AdaSmooth is its faster convergence rate and insensitivity to hyperparameters.
Parametric Exponential Linear Unit
Parameterized Exponential Linear Units, or PELU, is an activation function for neural networks. It involves learning a parameterization of ELU in order to learn the proper activation shape at each layer in a CNN. The PELU has two additional parameters over the ELU: Where , , and . Here causes a change in the slope in the positive quadrant, controls the scale of the exponential decay, and controls the saturation in the negative quadrant. Source: Activation Functions
Controllable Gate Adapter
This Uses similar blocks as of adapters but changes the way adapter activation works by adding a novel Activation function to it . This allows ConGater block to manually control the activation of the gates which results in continuous controll of any desired attributes inside the model.
In-Place Activated Batch Normalization
In-Place Activated Batch Normalization, or InPlace-ABN, substitutes the conventionally used succession of BatchNorm + Activation layers with a single plugin layer, hence avoiding invasive framework surgery while providing straightforward applicability for existing deep learning frameworks. It approximately halves the memory requirements during training of modern deep learning models.
The DELU is a type of activation function that has trainable parameters, uses the complex linear and exponential functions in the positive dimension and uses the SiLU in the negative dimension.
LeViT Attention Block is a module used for attention in the LeViT architecture. Its main feature is providing positional information within each attention block, i.e. where we explicitly inject relative position information in the attention mechanism. This is achieved by adding an attention bias to the attention maps.
DVD-GAN DBlock is a residual block for the discriminator used in the DVD-GAN architecture for video generation. Unlike regular residual blocks, 3D convolutions are employed due to the application to multiple frames in a video.
Spatio-Temporal Attention LSTM
In human action recognition, each type of action generally only depends on a few specific kinematic joints. Furthermore, over time, multiple actions may be performed. Motivated by these observations, Song et al. proposed a joint spatial and temporal attention network based on LSTM, to adaptively find discriminative features and keyframes. Its main attention-related components are a spatial attention sub-network, to select important regions, and a temporal attention sub-network, to select key frames. The spatial attention sub-network can be written as: \begin{align} s{t} &= U{s}\tanh(W{xs}X{t} + W{hs}h{t-1}^{s} + b{si}) + b{so} \end{align} \begin{align} \alpha{t} &= \text{Softmax}(s{t}) \end{align} \begin{align} Y{t} &= \alpha{t} X{t} \end{align} where is the input feature at time , , , , and are learnable parameters, and is the hidden state at step . Note that use of the hidden state means the attention process takes temporal relationships into consideration. The temporal attention sub-network is similar to the spatial branch and produces its attention map using: \begin{align} \beta{t} = \delta(W{xp}X{t} + W{hp}h{t-1}^{p} + b{p}). \end{align} It adopts a ReLU function instead of a normalization function for ease of optimization. It also uses a regularized objective function to improve convergence. Overall, this paper presents a joint spatiotemporal attention method to focus on important joints and keyframes, with excellent results on the action recognition task.
Locally-Grouped Self-Attention, or LSA, is a local attention mechanism used in the Twins-SVT architecture. Locally-grouped self-attention (LSA). Motivated by the group design in depthwise convolutions for efficient inference, we first equally divide the 2D feature maps into sub-windows, making self-attention communications only happen within each sub-window. This design also resonates with the multi-head design in self-attention, where the communications only occur within the channels of the same head. To be specific, the feature maps are divided into sub-windows. Without loss of generality, we assume and . Each group contains elements, and thus the computation cost of the self-attention in this window is , and the total cost is . If we let and , the cost can be computed as , which is significantly more efficient when and and grows linearly with if and are fixed. Although the locally-grouped self-attention mechanism is computation friendly, the image is divided into non-overlapping sub-windows. Thus, we need a mechanism to communicate between different sub-windows, as in Swin. Otherwise, the information would be limited to be processed locally, which makes the receptive field small and significantly degrades the performance as shown in our experiments. This resembles the fact that we cannot replace all standard convolutions by depth-wise convolutions in CNNs.
GBlock is a type of residual block used in the GAN-TTS text-to-speech architecture - it is a stack of two residual blocks. As the generator is producing raw audio (e.g. a 2s training clip corresponds to a sequence of 48000 samples), dilated convolutions are used to ensure that the receptive field of is large enough to capture long-term dependencies. The four kernel size-3 convolutions in each GBlock have increasing dilation factors: 1, 2, 4, 8. Convolutions are preceded by Conditional Batch Normalisation, conditioned on the linear embeddings of the noise term in the single-speaker case, or the concatenation of and a one-hot representation of the speaker ID in the multi-speaker case. The embeddings are different for each BatchNorm instance. A GBlock contains two skip connections, the first of which in GAN-TTS performs upsampling if the output frequency is higher than the input, and it also contains a size-1 convolution if the number of output channels is different from the input.
Phish: A Novel Hyper-Optimizable Activation Function
Deep-learning models estimate values using backpropagation. The activation function within hidden layers is a critical component to minimizing loss in deep neural-networks. Rectified Linear (ReLU) has been the dominant activation function for the past decade. Swish and Mish are newer activation functions that have shown to yield better results than ReLU given specific circumstances. Phish is a novel activation function proposed here. It is a composite function defined as f(x) = xTanH(GELU(x)), where no discontinuities are apparent in the differentiated graph on the domain observed. Generalized networks were constructed using different activation functions. SoftMax was the output function. Using images from MNIST and CIFAR-10 databanks, these networks were trained to minimize sparse categorical crossentropy. A large scale cross-validation was simulated using stochastic Markov chains to account for the law of large numbers for the probability values. Statistical tests support the research hypothesis stating Phish could outperform other activation functions in classification. Future experiments would involve testing Phish in unsupervised learning algorithms and comparing it to more activation functions.
Area Under the ROC Curve for Clustering
The area under the receiver operating characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we explore AUC as a performance measure in the unsupervised learning domain, more specifically, in the context of cluster analysis. In particular, we elaborate on the use of AUC as an internal/relative measure of clustering quality, which we refer to as Area Under the Curve for Clustering (AUCC). We show that the AUCC of a given candidate clustering solution has an expected value under a null model of random clustering solutions, regardless of the size of the dataset and, more importantly, regardless of the number or the (im)balance of clusters under evaluation. In addition, we elaborate on the fact that, in the context of internal/relative clustering validation as we consider, AUCC is actually a linear transformation of the Gamma criterion from Baker and Hubert (1975), for which we also formally derive a theoretical expected value for chance clusterings. We also discuss the computational complexity of these criteria and show that, while an ordinary implementation of Gamma can be computationally prohibitive and impractical for most real applications of cluster analysis, its equivalence with AUCC actually unveils a much more efficient algorithmic procedure. Our theoretical findings are supported by experimental results. These results show that, in addition to an effective and robust quantitative evaluation provided by AUCC, visual inspection of the ROC curves themselves can be useful to further assess a candidate clustering solution from a broader, qualitative perspective as well.
Introduced by Hinton et al. in 2012, dropout has stood the test of time as a regularizer for preventing overfitting in neural networks. In this study, we demonstrate that dropout can also mitigate underfitting when used at the start of training. During the early phase, we find dropout reduces the directional variance of gradients across mini-batches and helps align the mini-batch gradients with the entire dataset's gradient. This helps counteract the stochasticity of SGD and limit the influence of individual batches on model training. Our findings lead us to a solution for improving performance in underfitting models - early dropout: dropout is applied only during the initial phases of training, and turned off afterwards. Models equipped with early dropout achieve lower final training loss compared to their counterparts without dropout. Additionally, we explore a symmetric technique for regularizing overfitting models - late dropout, where dropout is not used in the early iterations and is only activated later in training. Experiments on ImageNet and various vision tasks demonstrate that our methods consistently improve generalization accuracy. Our results encourage more research on understanding regularization in deep learning and our methods can be useful tools for future neural network training, especially in the era of large data. Code is available at https://github.com/facebookresearch/dropout .
S-shaped ReLU
The S-shaped Rectified Linear Unit, or SReLU, is an activation function for neural networks. It learns both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is defined as a mapping: where , and are learnable parameters of the network and indicates that the SReLU can differ in different channels. The parameter represents the slope of the right line with input above a set threshold. and are thresholds in positive and negative directions respectively. Source: Activation Functions
Decomposition-Integration Class Activation Map
DecomCAM decomposes intermediate activation maps into orthogonal features using singular value decomposition and generates saliency maps by integrating them.
Lambda layers are a building block for modeling long-range dependencies in data. They consist of long-range interactions between a query and a structured set of context elements at a reduced memory cost. Lambda layers transform each available context into a linear function, termed a lambda, which is then directly applied to the corresponding query. Whereas self-attention defines a similarity kernel between the query and the context elements, a lambda layer instead summarizes contextual information into a fixed-size linear function (i.e. a matrix), thus bypassing the need for memory-intensive attention maps.
Quasi-Hyperbolic Momentum (QHM) is a stochastic optimization technique that alters momentum SGD with a momentum step, averaging an SGD step with a momentum step: The authors suggest a rule of thumb of and .
Hybrid Air-Water Temperature Difference
The hybrid model couples existing macro-meteorological models developed for similar microclimates along with some minimal amount of locally-acquired meteorological and data. The hybrid model framework consists of two components, a baseline macro-meteorological model and a machine learning model trained on that baseline macro-meteorological model’s residual error over the locally-acquired training measurements.
Mechanism Transfer is a meta-distributional scenario for few-shot domain adaptation in which a data generating mechanism is invariant across domains. This transfer assumption can accommodate nonparametric shifts resulting in apparently different distributions while providing a solid statistical basis for domain adaptation.
Neural Network Compression Framework
Neural Network Compression Framework, or NNCF, is a Python-based framework for neural network compression with fine-tuning. It leverages recent advances of various network compression methods and implements some of them, namely quantization, sparsity, filter pruning and binarization. These methods allow producing more hardware-friendly models that can be efficiently run on general-purpose hardware computation units (CPU, GPU) or specialized deep learning accelerators.
Contextual Attention Block
The Contextual Attention Block (CAB) is a new plug-and-play module to model context awareness. It is simple and effective and can be integrated with any feed-forward neural network. CAB infers weights that multiply the feature maps according to their causal influence on the scene, modeling the co-occurrence of different objects in the image. You can place the CAB module at different bottlenecks to infuse a hierarchical context awareness into the model.
Problem Agnostic Speech Encoder +
PASE+ is a problem-agnostic speech encoder that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). An online speech distortion module is employed, that contaminates the input signals with a variety of random disturbances. A revised encoder is also proposed that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, the authors refine the set of workers used in self-supervision to encourage better cooperation.
Frequency channel attention networks
FCANet contains a novel multi-spectral channel attention module. Given an input feature map , multi-spectral channel attention first splits into many parts . Then it applies a 2D DCT to each part . Note that a 2D DCT can use pre-processing results to reduce computation. After processing each part, all results are concatenated into a vector. Finally, fully connected layers, ReLU activation and a sigmoid are used to get the attention vector as in an SE block. This can be formulated as: \begin{align} s = F\text{fca}(X, \theta) & = \sigma (W{2} \delta (W{1}[(\text{DCT}(\text{Group}(X)))])) \end{align} \begin{align} Y & = s X \end{align} where indicates dividing the input into many groups and is the 2D discrete cosine transform. This work based on information compression and discrete cosine transforms achieves excellent performance on the classification task.
Liu et al. presented self-calibrated convolution as a means to enlarge the receptive field at each spatial location. Self-calibrated convolution is used together with a standard convolution. It first divides the input feature into and in the channel domain. The self-calibrated convolution first uses average pooling to reduce the input size and enlarge the receptive field: \begin{align} T{1} = AvgPool{r}(X{1}) \end{align} where is the filter size and stride. Then a convolution is used to model the channel relationship and a bilinear interpolation operator is used to upsample the feature map: \begin{align} X'{1} = \text{Up}(Conv2(T1)) \end{align} Next, element-wise multiplication finishes the self-calibrated process: \begin{align} Y'{1} = Conv3(X1) \sigma(X1 + X'1) \end{align} Finally, the output feature map of is formed: \begin{align} Y{1} &= Conv4(Y'{1}) \end{align} \begin{align} Y2 &= Conv1(X2) \end{align} \begin{align} Y &= [Y1; Y2] \end{align} Such self-calibrated convolution can enlarge the receptive field of a network and improve its adaptability. It achieves excellent results in image classification and certain downstream tasks such as instance segmentation, object detection and keypoint detection.
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Margin Rectified Linear Unit
Margin Rectified Linear Unit, or Margin ReLU, is a type of activation function based on a ReLU, but it has a negative threshold for negative values instead of a zero threshhold.
CTAB-GAN is a model for conditional tabular data generation. The generator and discriminator utilize the DCGAN architecture. An auxiliary classifier is also used with an MLP architecture.
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An All-Attention Layer is an attention module and layer for transformers that merges the self-attention and feedforward sublayers into a single unified attention layer. As opposed to the two-step mechanism of the Transformer layer, it directly builds its representation from the context and a persistent memory block without going through a feedforward transformation. The additional persistent memory block stores, in the form of key-value vectors, information that does not depend on the context. In terms of parameters, these persistent key-value vectors replace the feedforward sublayer.
Log-time and Log-space Extreme Classification
LTLS is a technique for multiclass and multilabel prediction that can perform training and inference in logarithmic time and space. LTLS embeds large classification problems into simple structured prediction problems and relies on efficient dynamic programming algorithms for inference. It tackles extreme multi-class and multi-label classification problems where the size of the output space is extremely large.
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.
ReGLU is an activation function which is a variant of GLU. The definition is as follows:
Adaptive Locally Connected Neuron
The Adaptive Locally Connected Neuron (ALCN) is a topology aware, and locally adaptive -focusing neuron:
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
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.
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.