5,489 machine learning methods and techniques
Orthogonal Regularization is a regularization technique for convolutional neural networks, introduced with generative modelling as the task in mind. Orthogonality is argued to be a desirable quality in ConvNet filters, partially because multiplication by an orthogonal matrix leaves the norm of the original matrix unchanged. This property is valuable in deep or recurrent networks, where repeated matrix multiplication can result in signals vanishing or exploding. To try to maintain orthogonality throughout training, Orthogonal Regularization encourages weights to be orthogonal by pushing them towards the nearest orthogonal manifold. The objective function is augmented with the cost: Where indicates a sum across all filter banks, is a filter bank, and is the identity matrix
Sparsemax is a type of activation/output function similar to the traditional softmax, but able to output sparse probabilities.
A Selective Kernel unit is a bottleneck block consisting of a sequence of 1×1 convolution, SK convolution and 1×1 convolution. It was proposed as part of the SKNet CNN architecture. In general, all the large kernel convolutions in the original bottleneck blocks in ResNeXt are replaced by the proposed SK convolutions, enabling the network to choose appropriate receptive field sizes in an adaptive manner. In SK units, there are three important hyper-parameters which determine the final settings of SK convolutions: the number of paths that determines the number of choices of different kernels to be aggregated, the group number that controls the cardinality of each path, and the reduction ratio that controls the number of parameters in the fuse operator. One typical setting of SK convolutions is to be .
Collaborative Preference Embedding
CPE is an effective collaborative metric learning to effectively address the problem of sparse and insufficient preference supervision from the margin distribution point-of-view.
Manifold Mixup is a regularization method that encourages neural networks to predict less confidently on interpolations of hidden representations. It leverages semantic interpolations as an additional training signal, obtaining neural networks with smoother decision boundaries at multiple levels of representation. As a result, neural networks trained with Manifold Mixup learn class-representations with fewer directions of variance. Consider training a deep neural network , where denotes the part of the neural network mapping the input data to the hidden representation at layer , and denotes the part mapping such hidden representation to the output . Training using Manifold Mixup is performed in five steps: (1) Select a random layer from a set of eligible layers in the neural network. This set may include the input layer . (2) Process two random data minibatches and as usual, until reaching layer . This provides us with two intermediate minibatches and . (3) Perform Input Mixup on these intermediate minibatches. This produces the mixed minibatch: where . Here, are one-hot labels, and the mixing coefficient as in mixup. For instance, is equivalent to sampling . (4) Continue the forward pass in the network from layer until the output using the mixed minibatch . (5) This output is used to compute the loss value and gradients that update all the parameters of the neural network.
tabular data Prior-data Fitted Network
We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods. TabPFN is fully entailed in the weights of our network, which accepts training and test samples as a set-valued input and yields predictions for the entire test set in a single forward pass. TabPFN is a Prior-Data Fitted Network (PFN) and is trained offline once, to approximate Bayesian inference on synthetic datasets drawn from our prior. This prior incorporates ideas from causal reasoning: It entails a large space of structural causal models with a preference for simple structures. On the 18 datasets in the OpenML-CC18 suite that contain up to 1 000 training data points, up to 100 purely numerical features without missing values, and up to 10 classes, we show that our method clearly outperforms boosted trees and performs on par with complex state-of-the-art AutoML systems with up to 230× speedup. This increases to a 5 700× speedup when using a GPU. We also validate these results on an additional 67 small numerical datasets from OpenML. We provide all our code, the trained TabPFN, an interactive browser demo and a Colab notebook at https://github.com/automl/TabPFN.
Location Sensitive Attention is an attention mechanism that extends the additive attention mechanism to use cumulative attention weights from previous decoder time steps as an additional feature. This encourages the model to move forward consistently through the input, mitigating potential failure modes where some subsequences are repeated or ignored by the decoder. Starting with additive attention where is a sequential representation from a BiRNN encoder and is the -th state of a recurrent neural network (e.g. a LSTM or GRU): where and are vectors, and are matrices. We extend this to be location-aware by making it take into account the alignment produced at the previous step. First, we extract vectors for every position of the previous alignment by convolving it with a matrix : These additional vectors are then used by the scoring mechanism :
Dataset pruning is an approach to reduce a large dataset to obtain a small dataset by removing less significant sample.
The Educational Competition Optimizer
In recent research, metaheuristic strategies stand out as powerful tools for complex optimization, capturing widespread attention. This study proposes the Educational Competition Optimizer (ECO), an algorithm created for diverse optimization tasks. ECO draws inspiration from the competitive dynamics observed in real-world educational resource allocation scenarios, harnessing this principle to refine its search process. To further boost its efficiency, the algorithm divides the iterative process into three distinct phases: elementary, middle, and high school. Through this stepwise approach, ECO gradually narrows down the pool of potential solutions, mirroring the gradual competition witnessed within educational systems. This strategic approach ensures a smooth and resourceful transition between ECO's exploration and exploitation phases. The results indicate that ECO attains its peak optimization performance when configured with a population size of 40. Notably, the algorithm's optimization efficacy does not exhibit a strictly linear correlation with population size. To comprehensively evaluate ECO's effectiveness and convergence characteristics, we conducted a rigorous comparative analysis, comparing ECO against nine state-of-the-art metaheuristic algorithms. ECO's remarkable success in efficiently addressing complex optimization problems underscores its potential applicability across diverse real-world domains. The additional resources and open-source code for the proposed ECO can be accessed at https://aliasgharheidari.com/ECO.html and https://github.com/junbolian/ECO.
Multi-Head Linear Attention is a type of linear multi-head self-attention module, proposed with the Linformer architecture. The main idea is to add two linear projection matrices when computing key and value. We first project the original -dimensional key and value layers and into -dimensional projected key and value layers. We then compute a dimensional context mapping using scaled-dot product attention: Finally, we compute context embeddings for each head using .
There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success. However, network training becomes more difficult with increasing depth and training of very deep networks remains an open problem. In this extended abstract, we introduce a new architecture designed to ease gradient-based training of very deep networks. We refer to networks with this architecture as highway networks, since they allow unimpeded information flow across several layers on "information highways". The architecture is characterized by the use of gating units which learn to regulate the flow of information through a network. Highway networks with hundreds of layers can be trained directly using stochastic gradient descent and with a variety of activation functions, opening up the possibility of studying extremely deep and efficient architectures.
Rank-One Model Editing
Neural Architecture Search (NAS) learns a modular architecture which can be transferred from a small dataset to a large dataset. The method does this by reducing the problem of learning best convolutional architectures to the problem of learning a small convolutional cell. The cell can then be stacked in series to handle larger images and more complex datasets. Note that this refers to the original method referred to as NAS - there is also a broader category of methods called "neural architecture search".
Just as dropout prevents co-adaptation of activations, DropPath prevents co-adaptation of parallel paths in networks such as FractalNets by randomly dropping operands of the join layers. This discourages the network from using one input path as an anchor and another as a corrective term (a configuration that, if not prevented, is prone to overfitting). Two sampling strategies are: - Local: a join drops each input with fixed probability, but we make sure at least one survives. - Global: a single path is selected for the entire network. We restrict this path to be a single column, thereby promoting individual columns as independently strong predictors.
Neighborhood Attention is a restricted self attention pattern in which each token's receptive field is limited to its nearest neighboring pixels. It was proposed in Neighborhood Attention Transformer as an alternative to other local attention mechanisms used in Hierarchical Vision Transformers. NA is in concept similar to stand alone self attention (SASA), in that both can be implemented with a raster scan sliding window operation over the key value pair. However, NA would require a modification to handle corner pixels, which helps maintain a fixed receptive field size and an increased number of relative positions. The primary challenge in experimenting with both NA and SASA has been computation. Simply extracting key values for each query is slow, takes up a large amount of memory, and is eventually intractable at scale. NA was therefore implemented through a new CUDA extension to PyTorch, NATTEN.
Slime Mould Algorithm
Slime Mould Algorithm (SMA) is a new stochastic optimizer proposed based on the oscillation mode of slime mould in nature. SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. 🔗 The source codes of SMA are publicly available at https://aliasgharheidari.com/SMA.html
Shifted Softplus is an activation function , which SchNet employs as non-linearity throughout the network in order to obtain a smooth potential energy surface. The shifting ensures that and improves the convergence of the network. This activation function shows similarity to ELUs, while having infinite order of continuity.
A Switch FFN is a sparse layer that operates independently on tokens within an input sequence. It is shown in the blue block in the figure. We diagram two tokens ( = “More” and = “Parameters” below) being routed (solid lines) across four FFN experts, where the router independently routes each token. The switch FFN layer returns the output of the selected FFN multiplied by the router gate value (dotted-line).
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces the computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating the state-of-the-art performance compared to recent methods for set-structured data.
Attention with Linear Biases
ALiBi, or Attention with Linear Biases, is a positioning method that allows Transformer language models to consume, at inference time, sequences which are longer than the ones they were trained on. ALiBi does this without using actual position embeddings. Instead, computing the attention between a certain key and query, ALiBi penalizes the attention value that that query can assign to the key depending on how far away the key and query are. So when a key and query are close by, the penalty is very low, and when they are far away, the penalty is very high. This method was motivated by the simple reasoning that words that are close-by matter much more than ones that are far away. This method is as fast as the sinusoidal or absolute embedding methods (the fastest positioning methods there are). It outperforms those methods and Rotary embeddings when evaluating sequences that are longer than the ones the model was trained on (this is known as extrapolation).
Model Soups
Compress an ensemble of models into a single one by averaging their weights (under certain pre-conditions).
A Dynamic Memory Network is a neural network architecture which processes input sequences and questions, forms episodic memories, and generates relevant answers. Questions trigger an iterative attention process which allows the model to condition its attention on the inputs and the result of previous iterations. These results are then reasoned over in a hierarchical recurrent sequence model to generate answers. The DMN consists of a number of modules: - Input Module: The input module encodes raw text inputs from the task into distributed vector representations. The input takes forms like a sentence, a long story, a movie review and so on. - Question Module: The question module encodes the question of the task into a distributed vector representation. For question answering, the question may be a sentence such as "Where did the author first fly?". The representation is fed into the episodic memory module, and forms the basis, or initial state, upon which the episodic memory module iterates. - Episodic Memory Module: Given a collection of input representations, the episodic memory module chooses which parts of the inputs to focus on through the attention mechanism. It then produces a ”memory” vector representation taking into account the question as well as the previous memory. Each iteration provides the module with newly relevant information about the input. In other words, the module has the ability to retrieve new information, in the form of input representations, which were thought to be irrelevant in previous iterations. - Answer Module: The answer module generates an answer from the final memory vector of the memory module.
Ensemble clustering, also called consensus clustering, has been attracting much attention in recent years, aiming to combine multiple base clustering algorithms into a better and more consensus clustering. Due to its good performance, ensemble clustering plays a vital role in many research areas, such as community detection and bioinformatics.
Self-Cure Network
Self-Cure Network, or SCN, is a method for suppressing uncertainties for large-scale facial expression recognition, prventing deep networks from overfitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight each training sample with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group.
Population Based Training, or PBT, is an optimization method for finding parameters and hyperparameters, and extends upon parallel search methods and sequential optimisation methods. It leverages information sharing across a population of concurrently running optimisation processes, and allows for online propagation/transfer of parameters and hyperparameters between members of the population based on their performance. Furthermore, unlike most other adaptation schemes, the method is capable of performing online adaptation of hyperparameters -- which can be particularly important in problems with highly non-stationary learning dynamics, such as reinforcement learning settings. PBT is decentralised and asynchronous, although it could also be executed semi-serially or with partial synchrony if there is a binding budget constraint.
Inception-ResNet-v2-B is an image model block for a 17 x 17 grid used in the Inception-ResNet-v2 architecture. It largely follows the idea of Inception modules - and grouped convolutions - but also includes residual connections.
Inception-ResNet-v2-C is an image model block for an 8 x 8 grid used in the Inception-ResNet-v2 architecture. It largely follows the idea of Inception modules - and grouped convolutions - but also includes residual connections.
Neural Additive Model
Neural Additive Models (NAMs) make restrictions on the structure of neural networks, which yields a family of models that are inherently interpretable while suffering little loss in prediction accuracy when applied to tabular data. Methodologically, NAMs belong to a larger model family called Generalized Additive Models (GAMs). NAMs learn a linear combination of networks that each attend to a single input feature: each in the traditional GAM formulationis parametrized by a neural network. These networks are trained jointly using backpropagation and can learn arbitrarily complex shape functions. Interpreting NAMs is easy as the impact of a feature on the prediction does not rely on the other features and can be understood by visualizing its corresponding shape function (e.g., plotting vs. ).
ProxylessNAS directly learns neural network architectures on the target task and target hardware without any proxy task. Additional contributions include: - Using a new path-level pruning perspective for neural architecture search, showing a close connection between NAS and model compression. Memory consumption is saved by one order of magnitude by using path-level binarization. - Using a novel gradient-based approach (latency regularization loss) for handling hardware objectives (e.g. latency). Given different hardware platforms: CPU/GPU/Mobile, ProxylessNAS enables hardware-aware neural network specialization that’s exactly optimized for the target hardware.
PrIme Sample Attention
PrIme Sample Attention (PISA) directs the training of object detection frameworks towards prime samples. These are samples that play a key role in driving the detection performance. The authors define Hierarchical Local Rank (HLR) as a metric of importance. Specifically, they use IoU-HLR to rank positive samples and ScoreHLR to rank negative samples in each mini-batch. This ranking strategy places the positive samples with highest IoUs around each object and the negative samples with highest scores in each cluster to the top of the ranked list and directs the focus of the training process to them via a simple re-weighting scheme. The authors also devise a classification-aware regression loss to jointly optimize the classification and regression branches. Particularly, this loss would suppress those samples with large regression loss, thus reinforcing the attention to prime samples.
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.
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.
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.
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.
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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.
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.
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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.
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.
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 .
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.
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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.
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.