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VAE Approximation Error: ELBO and Exponential Families | 1 INTRODUCTION . Variational autoencoders proposed by Kingma & Welling ( 2014 ) strive at learning complex data distributions pd ( x ) , x ∈ X in a generative way . They introduce latent variables z ∈ Z and model the joint distribution as pθ ( x | z ) p ( z ) , where p ( z ) is a simple distribution that is usually ass... | The paper studies the VAE approximation error, when the encoder and decoder distributions are from conditional exponential families (EFs). Theorem 1 characterizes the form of the joint probability (an EF-Harmonium) which is consistent, i.e. where q_{phi}(z|x) \equiv p_{theta}(z|x). Sec 4.2 shows a v interesting example... | SP:2d237edc34601e158d7ed48ecc72bc873ae5f4dd |
Learning Representation from Neural Fisher Kernel with Low-rank Approximation | 1 INTRODUCTION . Modern deep learning systems rely on finding good representations of data . For supervised learning models with feed forward neural networks , representations can naturally be equated with the activations of each layer . Empirically , the community has developed a set of effective heuristics for repres... | The paper investigates the representation of modern neural networks from the perspective of kernels by extracting features from pre-trained network models. The authors show the effectiveness of the proposed neural fisher kernel (NFK) for both unsupervised and supervised learning tasks. A low-rank approximation strategy... | SP:1a9ccd94a645d76c015a116960899f08d3faaefe |
Learning Representation from Neural Fisher Kernel with Low-rank Approximation | 1 INTRODUCTION . Modern deep learning systems rely on finding good representations of data . For supervised learning models with feed forward neural networks , representations can naturally be equated with the activations of each layer . Empirically , the community has developed a set of effective heuristics for repres... | The paper proposes a new strategy for extracting compact and intuitive representations of the data from a neural network. The approach exploits the feature map associated with the kernel representation of a neural network. In particular, the authors consider the Neural Fisher Kernel and propose a series of linear appro... | SP:1a9ccd94a645d76c015a116960899f08d3faaefe |
Adversarial Collaborative Learning on Non-IID Features | 1 INTRODUCTION . Deep learning is data hungry . While data are always dispersed in multiple parties ( e.g. , mobile devices , hospitals ) in reality , data are not allowed to transfer to a central server for training due to privacy concerns and data regulations . Collaborative learning among multiple parties without th... | This paper focuses on federated learning on non-IID features. This is a crucial problem when applying federated learning. The authors propose a new federated learning scheme, called ADCOL (Adversarial Collaborative Learning) for non-IID features. Specifically, the server is designed to train a discriminator to distingu... | SP:8347824ba3fc1c854115f6a776ac159d9978071a |
Adversarial Collaborative Learning on Non-IID Features | 1 INTRODUCTION . Deep learning is data hungry . While data are always dispersed in multiple parties ( e.g. , mobile devices , hospitals ) in reality , data are not allowed to transfer to a central server for training due to privacy concerns and data regulations . Collaborative learning among multiple parties without th... | This paper studies federated learning when different devices have non-iid features. To address the heterogeneity problem, it proposes a federated learning scheme called ADCOL based on adversarial learning. In ADCOL, the devices transfer local representations to the server while sending the discriminator to the device... | SP:8347824ba3fc1c854115f6a776ac159d9978071a |
Discovering Invariant Rationales for Graph Neural Networks | 1 INTRODUCTION . The eye-catching success in graph neural networks ( GNNs ) ( Hamilton et al. , 2017 ; Kipf & Welling , 2017 ; Dwivedi et al. , 2020 ) provokes the rationalization task , answering “ What knowledge drives the model to make certain predictions ? ” . The goal of selective rationalization ( aka . feature a... | This paper proposes a novel algorithm DIR that allows the learned GNN model to make predictions based on causal patterns. The whole framework consists of four components where (1) the rationale generator splits the input into causal and spurious parts; (2) the distribution intervener replaces the spurious part; (3) the... | SP:bf66a9d224ff5090ed67cfb1a6a73a9c929e5b50 |
Discovering Invariant Rationales for Graph Neural Networks | 1 INTRODUCTION . The eye-catching success in graph neural networks ( GNNs ) ( Hamilton et al. , 2017 ; Kipf & Welling , 2017 ; Dwivedi et al. , 2020 ) provokes the rationalization task , answering “ What knowledge drives the model to make certain predictions ? ” . The goal of selective rationalization ( aka . feature a... | This paper proposes to address the limitation of current interpretable graph neural networks over out-of-distribution data. In this work, the intrinsically interpretable GNN is investigated by identifying the invariant rationales corresponding to environment-invariant causal patterns. The proposed model is evaluated on... | SP:bf66a9d224ff5090ed67cfb1a6a73a9c929e5b50 |
Gradient-based Meta-solving and Its Applications to Iterative Methods for Solving Differential Equations | 1 INTRODUCTION . It is common and important in science and engineering to solve similar problems repeatedly . For example , in material science , a tremendous amount of physical and numerical experiments are conducted to discover and characterize new materials ( Schmidt et al . ( 2019 ) ) . For another example , in com... | This paper proposes leveraging data from previous problem instances to improve efficiency of solving similar ones in the future. A general gradient-based method is proposed, which is applied to generating initial guesses to differential equation solutions. This problem is formulated as a meta-learning problem. | SP:664054eccfdebcb9ea3ac78f3d89501dc3f8a352 |
Gradient-based Meta-solving and Its Applications to Iterative Methods for Solving Differential Equations | 1 INTRODUCTION . It is common and important in science and engineering to solve similar problems repeatedly . For example , in material science , a tremendous amount of physical and numerical experiments are conducted to discover and characterize new materials ( Schmidt et al . ( 2019 ) ) . For another example , in com... | The paper proposed a gradient-based algorithm GBMS to solve PDEs based on the solutions of other similar problems. In GBMS, a network is trained to produce good initial guess for the iterative solver of the PDE. Numerical experiments are performed to show the effectiveness of the method. | SP:664054eccfdebcb9ea3ac78f3d89501dc3f8a352 |
Prototype memory and attention mechanisms for few shot image generation | 1 INTRODUCTION . Recent neurophysiological findings based on calcium imaging have revealed that many neurons in the superficial layers of V1 are strongly tuned to complex local patterns , rather than simple oriented edges or bars . These complex neurons exhibit far stronger responses ( a 3-5 fold increase ) when expose... | In this paper, the authors propose a new “grandmother cell”-like memory mechanism for improving image generation performance in GANs. In short, this method clusters and stores activation vectors observed during training. Then at image generation time, activation vectors are augmented with the sum of the stored memories... | SP:5b88514d3eba834efba72c605496c4c057c30387 |
Prototype memory and attention mechanisms for few shot image generation | 1 INTRODUCTION . Recent neurophysiological findings based on calcium imaging have revealed that many neurons in the superficial layers of V1 are strongly tuned to complex local patterns , rather than simple oriented edges or bars . These complex neurons exhibit far stronger responses ( a 3-5 fold increase ) when expose... | Paper proposes a novel prototype-based memory modulation layer (MoCA) to improve the generator network of a GAN. The target problem is few-shot image generation. Memory is arranged hierarchically into prototype semantic cells and prototype component cells. This design is loosely inspired by the recent discovery of "gra... | SP:5b88514d3eba834efba72c605496c4c057c30387 |
Better Supervisory Signals by Observing Learning Paths | 1 INTRODUCTION . In multi-class classification problems , we usually supervise our model with “ one-hot ” labels : label vectors y which have yi = 1 for one i , and 0 for all other dimensions . Over time , however , it has gradually become clear that this “ default ” setup is not always the best choice in practice , as... | This paper proposes an explanation for the success of distillation. It first experiments with synthetic Gaussian data. On synthetic data, it shows that distillation works better from an early-stopped model. It probes why, and finds that, when the one-hot label is far from the true conditional distribution, the early-st... | SP:781801713deb9efac5404cb98f6c40c83244cc14 |
Better Supervisory Signals by Observing Learning Paths | 1 INTRODUCTION . In multi-class classification problems , we usually supervise our model with “ one-hot ” labels : label vectors y which have yi = 1 for one i , and 0 for all other dimensions . Over time , however , it has gradually become clear that this “ default ” setup is not always the best choice in practice , as... | This paper introduces a method for doing knowledge distillation with noisy labels. The contribution consists on representing distilled labels as a weighted moving average of the predicted labels from a teacher as it trains (using noisy one-hot encoded labels). Finally, a student is trained using the distilled labels. | SP:781801713deb9efac5404cb98f6c40c83244cc14 |
Group equivariant neural posterior estimation | 1 INTRODUCTION . Bayesian inference provides a means of characterizing a system by comparing models against data . Given a forward model or likelihood p ( x|θ ) for data x described by parameters θ , and a prior p ( θ ) , the Bayesian posterior is proportional to the product , p ( θ|x ) ∝ p ( x|θ ) p ( θ ) . Sampling t... | The authors propose group equivariant neural posterior estimation (GNPE), a posterior estimation method which can self-consistently infer parameters and standardize the pose. For equivariant posterior distributions, the GNPE can achieve better performance than the traditional NPE. Moreover, GNPE can also be applied to ... | SP:b0f193db92cf2541bc252cb492ee3f9a6d50fc16 |
Group equivariant neural posterior estimation | 1 INTRODUCTION . Bayesian inference provides a means of characterizing a system by comparing models against data . Given a forward model or likelihood p ( x|θ ) for data x described by parameters θ , and a prior p ( θ ) , the Bayesian posterior is proportional to the product , p ( θ|x ) ∝ p ( x|θ ) p ( θ ) . Sampling t... | In this paper the authors present a method for performing Bayesian inference in likehood-free settings where the data and parameters are jointly equivariant or approximately equivariant. Examples include translational or rotational equivariance, where if the latent parameter are translate or rotated, then the distribu... | SP:b0f193db92cf2541bc252cb492ee3f9a6d50fc16 |
Spatial Frequency Sensitivity Regularization for Robustness | 1 INTRODUCTION . While deep neural networks ( DNN ) achieve remarkable performance on many challenging image classification tasks , they can suffer significant drops in performance when evaluated on out-ofdistribution ( o.o.d . ) data . Intriguingly , this lack of robustness has been partially attributed to the frequen... | This paper proposes a novel spatial frequency regularization technique that improves the robustness of training neural networks against superficial fourier statistics in a dataset. In the loss function, It adds a regularization term that is based on the Fourier-transformed input-Jacobian. This term could be customize... | SP:cb6397f78128e20abf726c63700dd49370335418 |
Spatial Frequency Sensitivity Regularization for Robustness | 1 INTRODUCTION . While deep neural networks ( DNN ) achieve remarkable performance on many challenging image classification tasks , they can suffer significant drops in performance when evaluated on out-ofdistribution ( o.o.d . ) data . Intriguingly , this lack of robustness has been partially attributed to the frequen... | This paper proposes a measure for a model’s spatial frequency sensitivity (SFS) based on the input-Jacobian in the Fourier basis. With this measure, the authors observe standard CNN training biases towards certain particular spatial frequencies consistently across samples. Based on this measure, the authors propose a f... | SP:cb6397f78128e20abf726c63700dd49370335418 |
Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks | Understanding how activity in neural circuits reshapes following task learning could reveal fundamental mechanisms of learning . Thanks to the recent advances in neural imaging technologies , high-quality recordings can be obtained from hundreds of neurons over multiple days or even weeks . However , the complexity and... | This paper uses CycleGAN to map neuronal activities of mice (as measured by Calcium traces) pre- and post-learning. The main contributions are (1) empirical results of using CycleGAN to learn the pre- and post-learning mapping look promising. (2) using both attention mask (which is for gating residual concatenations) a... | SP:1640224fbb539102c940e326404114dfdd4abf98 |
Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks | Understanding how activity in neural circuits reshapes following task learning could reveal fundamental mechanisms of learning . Thanks to the recent advances in neural imaging technologies , high-quality recordings can be obtained from hundreds of neurons over multiple days or even weeks . However , the complexity and... | This paper presents a new method for learning the transformation in neural population activity that takes place during task learning. The method is based on CycleGAN, but includes additional modifications related to neural data and the manner in which it is collected. The paper also presents visual interrogations of th... | SP:1640224fbb539102c940e326404114dfdd4abf98 |
Provably Calibrated Regression Under Distribution Drift | 1 INTRODUCTION . Accurate uncertainty quantification is crucial for machine learning predictions used in high-stakes decision making . Typically , uncertainty is represented by probability distributions over the possible outcomes , and these probabilities should be calibrated . In the regression setup , for example , t... | This paper considers the problem of calibrating quantiles outputted by a regression model. The data is assumed to draw from a non-iid source (e.g. time series with distribution shift). The paper proposes a method that adjusts the output quantiles to be better calibrated for a less-restricted definition of b-calibrated.... | SP:221f1dde4d5baeae63e928e94f8759a7b4b1c926 |
Provably Calibrated Regression Under Distribution Drift | 1 INTRODUCTION . Accurate uncertainty quantification is crucial for machine learning predictions used in high-stakes decision making . Typically , uncertainty is represented by probability distributions over the possible outcomes , and these probabilities should be calibrated . In the regression setup , for example , t... | For real-world applications where ML models are used, quantifying predictive uncertainty is an essential task as, for example, in safety-critical applications misclassification might have disastrous consequences. On the theory side, the analysis is typically performed under the i.i.d. assumption which could be unsatisf... | SP:221f1dde4d5baeae63e928e94f8759a7b4b1c926 |
Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction | 1 INTRODUCTION . Network/Graph representation learning ( a.k.a . embedding ) aims to preserve the high-dimensional complex graph information involving node features and link structures in a low-dimensional embedding space , which requires effective feature selection and dimension reduction ( Hamilton et al. , 2017b ) .... | This paper studies the problem of graph representation learning with graph autoencoder. The paper argues that most GNNs are designed for semi-supervised learning and cannot learn task-agnostic embedding. As a result, the paper proposes a graph autoencoder architecture that trains the GNN in an unsupervised manner. The ... | SP:9afba78dbb1607966ebd8b973563bb07efd8377b |
Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction | 1 INTRODUCTION . Network/Graph representation learning ( a.k.a . embedding ) aims to preserve the high-dimensional complex graph information involving node features and link structures in a low-dimensional embedding space , which requires effective feature selection and dimension reduction ( Hamilton et al. , 2017b ) .... | The paper proposes a novel approach to graph representation learning. In particular, a graph auto-encoder is proposed that aims to better capture the topological structure by utilising a neighbourhood reconstruction and a degree reconstruction objective. An optimal-transport based objective is proposed for the neighbou... | SP:9afba78dbb1607966ebd8b973563bb07efd8377b |
Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection | 1 INTRODUCTION . Many real-world applications involve positive and unlabeled ( PU ) datasets in which only some of the data is labeled positive while the majority is unlabeled and contains both positives and negatives . PU learning aims to learn a binary classifier in this challenging setting without any labeled negati... | This paper proposes PUUPL, an uncertainty-aware pseudo-label selection method for positive-unlabeled (PU) learning. To improve the performance of pseudo-labeling, the authors suggest using the epistemic uncertainty (the difference between the entropy of the mean prediction and the mean of entropies of each prediction).... | SP:50de6aaf2c0749724bf725075d84a00021646310 |
Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection | 1 INTRODUCTION . Many real-world applications involve positive and unlabeled ( PU ) datasets in which only some of the data is labeled positive while the majority is unlabeled and contains both positives and negatives . PU learning aims to learn a binary classifier in this challenging setting without any labeled negati... | This paper studies the PU learning problem. It proposes a two-step approach that can estimate the pseudo-label uncertainty so that more reliable pseudo-labels can be assigned, which improves the predictive performance. The proposed estimation method is different from previous methods. | SP:50de6aaf2c0749724bf725075d84a00021646310 |
Stability analysis of SGD through the normalized loss function | 1 INTRODUCTION . In the last few years , deep learning has succeeded in establishing state-of-the-art performances in a wide variety of tasks in fields like computer vision , natural language processing and bioinformatics ( LeCun et al. , 2015 ) . Understanding when and how these networks generalize better is important... | This paper conducted a stability analysis of Stochastic Gradient Descent (SGD) for empirical risk minimization induced by the so-called normalized loss function. Here, the normalization is taken with respect to parameters involved in an individual loss; see (4) for the definition of the normalized loss function. The pa... | SP:9070183afc9422af7dcef84aea785cb59bbba3ae |
Stability analysis of SGD through the normalized loss function | 1 INTRODUCTION . In the last few years , deep learning has succeeded in establishing state-of-the-art performances in a wide variety of tasks in fields like computer vision , natural language processing and bioinformatics ( LeCun et al. , 2015 ) . Understanding when and how these networks generalize better is important... | This paper considers the problem of understanding the generalization of SGD using the stability framework. The well-known result in this line of work is the paper by Hardt'16. In Hardt'16, the stability is measured using the difference between the "actual" weights of two copy of SGD which differ in a single data point.... | SP:9070183afc9422af7dcef84aea785cb59bbba3ae |
Stability and Generalisation in Batch Reinforcement Learning | 1 INTRODUCTION . A central aim of machine learning theory is to provide wort-case bounds on overfitting . However , in reinforcement learning ( RL ) , which typically employs online learning on an unlimited stream of data and does not minimise an empirical risk , generalisation is more difficult to characterise than in... | The paper aims to study the effect of stability on generalization of a particular off-policy policy evaluation algorithm. Specifically, the authors study a version of Fitted Q-evaluation where the iterative procedure is instead thought of as a gradient update and is performed by only partially fitting the new Q-functio... | SP:90f90a6bb1e055d5cf386162c2e1d17a95d4db29 |
Stability and Generalisation in Batch Reinforcement Learning | 1 INTRODUCTION . A central aim of machine learning theory is to provide wort-case bounds on overfitting . However , in reinforcement learning ( RL ) , which typically employs online learning on an unlimited stream of data and does not minimise an empirical risk , generalisation is more difficult to characterise than in... | As far as I can see, this work tries to leverage the connection between stability and generalization studied in supervised learning settings, and tries to build a similar connection for batch RL settings. To reason about stability in a batch RL problem, this work proposes a modified definition of stability that takes i... | SP:90f90a6bb1e055d5cf386162c2e1d17a95d4db29 |
Optimal Transport for Long-Tailed Recognition with Learnable Cost Matrix | 1 INTRODUCTION . Classification problems in the real world are generally challenged by the long-tailed label distribution , i.e. , having a small number of samples for a majority of labels , and a dominant number of samples for a minority of labels ( Van Horn & Perona , 2017 ; Buda et al. , 2018 ; Liu et al. , 2019 ) o... | This paper contributes an extension of post hoc correction of long-tailed recognition with Optimal Transport (OT). Unlike the previous work (e.g. logit adjustment) which focuses on sample-wise correction, this work, on the other hand, considers the marginal distribution of the overall data for correction. The method is... | SP:ac3bde5bb8c4674166478734b66c277fc74f5053 |
Optimal Transport for Long-Tailed Recognition with Learnable Cost Matrix | 1 INTRODUCTION . Classification problems in the real world are generally challenged by the long-tailed label distribution , i.e. , having a small number of samples for a majority of labels , and a dominant number of samples for a minority of labels ( Van Horn & Perona , 2017 ; Buda et al. , 2018 ; Liu et al. , 2019 ) o... | This paper proposes a new method for post-hoc correction in long-tailed recognition. Specifically, they leverage the idea of optimal transport (OT) and propose a linear mapping to replace the original exact cost matrix in OT problem. From the experiments, the proposed method can be combined with existing methods and bo... | SP:ac3bde5bb8c4674166478734b66c277fc74f5053 |
Learning Representation for Bayesian Optimization with Collision-free Regularization | Bayesian Optimization has been challenged by the large-scale and highdimensional datasets , which are common in real-world scenarios . Recent works attempt to handle such input by applying neural networks ahead of the classical Gaussian process to learn a ( low-dimensional ) latent representation . We show that even wi... | Despite its success, Gaussian process based Bayesian optimization still struggles in high dimensional search spaces. Current approaches aim to learn an embedding to optimize the objective in a low dimensional continuous latent space. This paper provides evidence that with current approaches, different data points in th... | SP:ec387dc36bcda590bbe0a3cf735b83b49616da3e |
Learning Representation for Bayesian Optimization with Collision-free Regularization | Bayesian Optimization has been challenged by the large-scale and highdimensional datasets , which are common in real-world scenarios . Recent works attempt to handle such input by applying neural networks ahead of the classical Gaussian process to learn a ( low-dimensional ) latent representation . We show that even wi... | This paper is concerned with latent space Bayesian optimisation that typically involves a step of learning a lower-dimensional latent representation. The authors focus on non-linear embeddings generated through neural networks. They observe a collision problem in the latent space and attempt to resolve it by introducin... | SP:ec387dc36bcda590bbe0a3cf735b83b49616da3e |
Concurrent Adversarial Learning for Large-Batch Training | 1 INTRODUCTION . With larger datasets and bigger models proposed , training neural networks has become quite timeconsuming . For instance , training BERT ( Devlin et al. , 2019 ) takes 3 days on 16 v3 TPUs . GPT-2 ( Radford et al. , 2019 ) contains 1,542M parameters and requires 168 hours of training on 16 v3 TPU chips... | This paper presents a simple algorithm named ConAdv to incorporate adversarial training into the large-batch training setting such that one can further increase the batch size without harming too much accuracy while maintaining the high utilization of the hardware. The core idea is to use adversarial training to improv... | SP:d203a332170f0f196a66a40e4b23ac99e07aeb7f |
Concurrent Adversarial Learning for Large-Batch Training | 1 INTRODUCTION . With larger datasets and bigger models proposed , training neural networks has become quite timeconsuming . For instance , training BERT ( Devlin et al. , 2019 ) takes 3 days on 16 v3 TPUs . GPT-2 ( Radford et al. , 2019 ) contains 1,542M parameters and requires 168 hours of training on 16 v3 TPU chips... | This manuscript empirically show that adversarial training in large-batch training scenario has better performance than traditional data augmentation. And furthermore, the authors proposed a simple method to conduct adversarial example generation and gradient computation w.r.t. to weights concurrently to accelerate the... | SP:d203a332170f0f196a66a40e4b23ac99e07aeb7f |
Source-Target Unified Knowledge Distillation for Memory-Efficient Federated Domain Adaptation on Edge Devices | To conduct local inference on edge devices , it is necessary to deploy compact machine learning models on such devices . When such a compact model is applied to a new environment , its inference accuracy can be degradedif the target data from the new environment have a different distribution fromthe source data used fo... | This paper considers a new and complex setting involving domain adaptation, federated learning, and knowledge distillation: Under the premise of protecting privacy, one needs to deploy a compact model from a source central server to target client devices and requires the model to learn new knowledge with target unlabel... | SP:792688942b9ebce1d30ed067109066ebeaf1236a |
Source-Target Unified Knowledge Distillation for Memory-Efficient Federated Domain Adaptation on Edge Devices | To conduct local inference on edge devices , it is necessary to deploy compact machine learning models on such devices . When such a compact model is applied to a new environment , its inference accuracy can be degradedif the target data from the new environment have a different distribution fromthe source data used fo... | A compact model deployed to a device may not work well if this device has a different data distribution. This work proposes to load a large pretrained model onto a device and then adapt it to the target data on the device. As directly training the full large model is too memory-heavy, this work proposes to adapt the la... | SP:792688942b9ebce1d30ed067109066ebeaf1236a |
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks | 1 INTRODUCTION . The unprecedented performance achieved by neural networks ( NNs ) , e.g. , convolutional neural networks ( CNNs ) and Transformers , requires intensive multiplications and thus prohibitive training and inference costs , contradicting the explosive demand for embedding various intelligent functionalitie... | This paper designs a hybrid search space that includes multiplication-based operators and multiplication-free operations to find good trading points between accuracy-efficiency. Further, this work defines the problem when training weight-sharing supernet on the hybrid search space and proposes the heterogenous weight s... | SP:e34072bff0b40655ed566bd88f75b458d381edc4 |
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks | 1 INTRODUCTION . The unprecedented performance achieved by neural networks ( NNs ) , e.g. , convolutional neural networks ( CNNs ) and Transformers , requires intensive multiplications and thus prohibitive training and inference costs , contradicting the explosive demand for embedding various intelligent functionalitie... | This paper proposes a NAS method for both multiplication-based and adder-based networks. The contributions mainly lie on hybrid search space and new weight sharing strategy. The experimental results shows the method can obtain energy-efficient networks with high performance. | SP:e34072bff0b40655ed566bd88f75b458d381edc4 |
Gradient-Guided Importance Sampling for Learning Discrete Energy-Based Models | 1 INTRODUCTION . Energy-Based models ( EBMs ) , also known as unnormalized probabilistic models , model distributions by associating unnormalized probability densities . Such methods have been developed for decades ( Hopfield , 1982 ; Ackley et al. , 1985 ; Cipra , 1987 ; Dayan et al. , 1995 ; Zhu et al. , 1998 ; Hinto... | This paper proposes a more efficient version of ratio matching (RM) for training discrete energy-based models. The proposed method subsamples the dimensions to use in the original RM objective and then uses importance sampling to reduce variance. The importance sampling distribution is based on a Taylor-series approxim... | SP:6d70759d66c94400778b2bad913422e2ea28dac5 |
Gradient-Guided Importance Sampling for Learning Discrete Energy-Based Models | 1 INTRODUCTION . Energy-Based models ( EBMs ) , also known as unnormalized probabilistic models , model distributions by associating unnormalized probability densities . Such methods have been developed for decades ( Hopfield , 1982 ; Ackley et al. , 1985 ; Cipra , 1987 ; Dayan et al. , 1995 ; Zhu et al. , 1998 ; Hinto... | The paper propose a Monte Carlo approximation for the expensive ratio matching method for learning discrete energy-based models. The key idea is to first write the ratio matching objective as an expectation wrt a uniform distribution, then use importance sampling for a more efficient estimation, where the optimal propo... | SP:6d70759d66c94400778b2bad913422e2ea28dac5 |
Delayed Geometric Discounts: An alternative criterion for Reinforcement Learning | 1 INTRODUCTION . In the infinite horizon setting , and without further assumptions on the underlying Markov Decision Process ( MDP ) , available RL algorithms learn optimal policies only in the sense of the discounted cumulative rewards ( Puterman , 2014 ) . While the geometric discounting is well suited to model a ter... | The paper proposes an optimality criterion for RL that is generalizes the standard exponential discount commonly used in a large volume of RL research. In particular, the paper proposes substituting the standard discount term $\gamma^t$ by a generalized discount term $\Phi_D(t)$ built from a set of discounts $\gamma_0,... | SP:27c8b1b419259661fd5388f4a53055f90fe0b4f9 |
Delayed Geometric Discounts: An alternative criterion for Reinforcement Learning | 1 INTRODUCTION . In the infinite horizon setting , and without further assumptions on the underlying Markov Decision Process ( MDP ) , available RL algorithms learn optimal policies only in the sense of the discounted cumulative rewards ( Puterman , 2014 ) . While the geometric discounting is well suited to model a ter... | **Summary.** This paper proposes a generalization to the typical geometric discounting scheme in reinforcement learning. In particular, a new _delayed_ discount criterion is proposed (Equation 1) that captures traditional geometric discounting when $D=0$, but also allows for other kinds of temporal preferences. Figure ... | SP:27c8b1b419259661fd5388f4a53055f90fe0b4f9 |
Disentangling Properties of Contrastive Methods | 1 INTRODUCTION . Learning a disentangled representation is a long-desired goal in the deep learning community ( Bengio et al. , 2013 ; Peters et al. , 2017 ; Goodfellow et al. , 2016 ; Bengio et al. , 2007 ; Schmidhuber , 1992 ; Lake et al. , 2017 ; Tschannen et al. , 2018 ) . A disentangled representation matches how ... | This paper makes an interesting empirical observation - BYOL representations have better disentanglement properties, according to some metrics, than current specialized methods. Furthermore, the authors found that the selection of the normalization function affects the results. The authors also claim the dimensions are... | SP:9240db905d01022b6dc03f6789c3c853d8c84b4b |
Disentangling Properties of Contrastive Methods | 1 INTRODUCTION . Learning a disentangled representation is a long-desired goal in the deep learning community ( Bengio et al. , 2013 ; Peters et al. , 2017 ; Goodfellow et al. , 2016 ; Bengio et al. , 2007 ; Schmidhuber , 1992 ; Lake et al. , 2017 ; Tschannen et al. , 2018 ) . A disentangled representation matches how ... | This work explores the disentangling properties of contrastive methods. The authors discover that contrastive methods, particularly BYOL, learns disentangled representations with just a change of normalization method in the encoder. The work also proposes a new concept called "group disentanglement", which is a relaxed... | SP:9240db905d01022b6dc03f6789c3c853d8c84b4b |
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks | The most significant barrier to the advancement of Neural Architecture Search ( NAS ) is its demand for large computational resources , which hinders scientifically sound empirical evaluations of NAS methods . Tabular NAS benchmarks have alleviated this problem substantially , making it possible to properly evaluate NA... | This work explored how to use surrogate models to expand the existing (and limited) neural architecture search -- NAS -- benchmark. The new expanded benchmark is named as surrogate NAS benchmark. All codes are open-sourced, which demonstrated the good reproducibility of this work. The authors have conducted extensive e... | SP:98df8621044599cf615f32412eb38d812d5be743 |
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks | The most significant barrier to the advancement of Neural Architecture Search ( NAS ) is its demand for large computational resources , which hinders scientifically sound empirical evaluations of NAS methods . Tabular NAS benchmarks have alleviated this problem substantially , making it possible to properly evaluate NA... | Overview: This work proposes a new NAS benchmark based on the results of surrogate models prediction. This surrogate model is able to predict all architectures in DARTS search space, which is about 10^18 possible architectures. The author compared the predict performance among different type of surrogate models and als... | SP:98df8621044599cf615f32412eb38d812d5be743 |
Creating Training Sets via Weak Indirect Supervision | 1 INTRODUCTION . One of the greatest bottlenecks of using modern machine learning models is the need for substantial amounts of manually-labeled training data . In real-world applications , such manual annotations are typically time-consuming , labor-intensive and static . To reduce the efforts of annotation , research... | This paper studied a weakly supervised classification problem, called **weak indirect supervision**, where the supervision signals are from labels that are different from but still informative of the classes. The author proposed a new two-step method that first creates probabilistic labels using an **exponential family... | SP:55199c8f21981b3956a07d485d040defc5ba3fa9 |
Creating Training Sets via Weak Indirect Supervision | 1 INTRODUCTION . One of the greatest bottlenecks of using modern machine learning models is the need for substantial amounts of manually-labeled training data . In real-world applications , such manual annotations are typically time-consuming , labor-intensive and static . To reduce the efforts of annotation , research... | The paper addresses a novel research problem of using "indirect" label sources in the weak supervision framework to create labelled datasets. The indirect label sources are similar to the labeling functions in prior works in weak supervision ( data programming) with one caveat that these sources produce labels from dif... | SP:55199c8f21981b3956a07d485d040defc5ba3fa9 |
Using Document Similarity Methods to create Parallel Datasets for Code Translation | Translating source code from one programming language to another is a critical , time-consuming task in modernizing legacy applications and codebases . Recent work in this space has drawn inspiration from the software naturalness hypothesis by applying natural language processing techniques towards automating the code ... | This paper mines noisy parallel datasets of code by calculating the similarity between two non-parallel sets of documents. The authors first show that the document similarity methods can indeed align parallel documents and find that the word movers distance (WMD) is the most effective one. Then, the authors show the hi... | SP:7d2e5993fea3dc4fc8090cfe569d8206a16f7bfb |
Using Document Similarity Methods to create Parallel Datasets for Code Translation | Translating source code from one programming language to another is a critical , time-consuming task in modernizing legacy applications and codebases . Recent work in this space has drawn inspiration from the software naturalness hypothesis by applying natural language processing techniques towards automating the code ... | This paper proposes to use similarity metric to generate pseudo alignments between source/target program pairs. Generated pairs are utilized to train program translation model. The paper described a simple greedy method to align both codes, and experimented 5 types of similarity metric as its inner measure. According t... | SP:7d2e5993fea3dc4fc8090cfe569d8206a16f7bfb |
Adaptive Filters for Low-Latency and Memory-Efficient Graph Neural Networks | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have emerged as an effective way to build models over arbitrarily structured data . For example , they have successfully been applied to computer vision tasks : GNNs can deliver high performance on point cloud data ( Qi et al. , 2017 ) and for feature matching across imag... | This paper claimed they designed a new GNN architecture that achieves state-of-the-art performance with lower memory consumption and latency. More specifically, the proposed model uses memory proportional to the number of vertices in the graph $O(V)$, in contrast to competing methods which require memory proportional t... | SP:707e8ce06a2315ede25190c7e4f5fc5e663d200f |
Adaptive Filters for Low-Latency and Memory-Efficient Graph Neural Networks | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have emerged as an effective way to build models over arbitrarily structured data . For example , they have successfully been applied to computer vision tasks : GNNs can deliver high performance on point cloud data ( Qi et al. , 2017 ) and for feature matching across imag... | This paper introduces Adaptive Filters that enable some of the benefits of Message Passing architectures, but while maintaining a memory consumption that scales with the number of nodes. The authors claim that this architecture is not only better performing, and use less memory, but more efficient for GPUs through the... | SP:707e8ce06a2315ede25190c7e4f5fc5e663d200f |
Graph Neural Networks with Learnable Structural and Positional Representations | 1 INTRODUCTION . GNNs have recently emerged as a powerful class of deep learning architectures to analyze datasets where information is present in the form of heteregeneous graphs that encode complex data connectivity . Experimentally , these architectures have shown great promises to be impactful in diverse domains su... | This paper is concerning the Positional Encoding (PE) for GNNs. PE augments the typical GNNs to distinguish isomorphic nodes. However, existing PE models such as Laplacian eigenvectors require huge computational resources. This manuscript proposes LSPE that augments the input to the nodes AND the embedding vectors wit... | SP:b9cbdab6989220afcdf7c836d52119d93997c3a1 |
Graph Neural Networks with Learnable Structural and Positional Representations | 1 INTRODUCTION . GNNs have recently emerged as a powerful class of deep learning architectures to analyze datasets where information is present in the form of heteregeneous graphs that encode complex data connectivity . Experimentally , these architectures have shown great promises to be impactful in diverse domains su... | This paper proposes a framework that utilizes Random Walk Positional Embeddings (RWPE) as extra features to boost the performance of GNNs. In particular, positional embeddings are updated as a separate forward network in each layer. The framework has demonstrated improved quality by injecting its positional embeddings ... | SP:b9cbdab6989220afcdf7c836d52119d93997c3a1 |
Understanding and Leveraging Overparameterization in Recursive Value Estimation | The theory of function approximation in reinforcement learning ( RL ) typically considers low capacity representations that incur a tradeoff between approximation error , stability and generalization . Current deep architectures , however , operate in an overparameterized regime where approximation error is not necessa... | This paper studies the convergence properties of three classical value estimation algorithms (TD, FVI and RM) under over-parameterized linear case. The difference among convergence results are interpreted unifiedly through different constraints in an optimization problem. It also proposes an generalization bound for FV... | SP:39cb14ce95091715f262868c8eed6b52e653cb06 |
Understanding and Leveraging Overparameterization in Recursive Value Estimation | The theory of function approximation in reinforcement learning ( RL ) typically considers low capacity representations that incur a tradeoff between approximation error , stability and generalization . Current deep architectures , however , operate in an overparameterized regime where approximation error is not necessa... | In this paper, the authors consider the overparameterized linear representations of TD, FVI, and RM. A unified interpretation of these algorithms of minimizing the Euclidean norm of the weights subject to alternative constraints is proposed. The paper is also supported by the empirical results. | SP:39cb14ce95091715f262868c8eed6b52e653cb06 |
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning | 1 INTRODUCTION . While deep learning has achieved great success in various applications , it was pointed out that there is a debate over the problem of systematicity in connectionist models ( Fodor & Pylyshyn , 1988 ; Fodor & McLaughlin , 1990 ; Hadley , 1994 ; Jansen & Watter , 2012 ; Dong et al. , 2018 ) . To concret... | This paper presents a novel method for rule induction. The main idea is to apply reinforcement learning in the task of relational pathfinding within a finite Herbrand base. The reinforcement learner uses MCTS to find the best routes to establish the path in between the two arguments of training examples, while the usef... | SP:81b19cb59aad98ed3845ab403a9468d9f7bb1445 |
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning | 1 INTRODUCTION . While deep learning has achieved great success in various applications , it was pointed out that there is a debate over the problem of systematicity in connectionist models ( Fodor & Pylyshyn , 1988 ; Fodor & McLaughlin , 1990 ; Hadley , 1994 ; Jansen & Watter , 2012 ; Dong et al. , 2018 ) . To concret... | This work explores a rule learning approach (R5) for 2 relation prediction tasks (CLUTRR and GraphLog). The proposed approach starts by finding connecting paths between the two query entities. Then it recursively merge relation pairs until it consists of a single relation output. The merging process is controlled by a ... | SP:81b19cb59aad98ed3845ab403a9468d9f7bb1445 |
Image Functions In Neural Networks: A Perspective On Generalization | In this work , we show that training with SGD on ReLU neural networks gives rise to a natural set of functions for each image that are not perfectly correlated until later in training . Furthermore , we show experimentally that the intersection of paths for different images also changes during the course of training . ... | This paper proposes a different look at why neural networks generalize despite optimizing to zero training error, over-parameterization, etc. The contribution is mostly experimental in the sense of computing various statistics of a model during training and correlating those statistics with generalization performance.... | SP:77e60bbe1d3357adcdbe9c340b2f081cbce95090 |
Image Functions In Neural Networks: A Perspective On Generalization | In this work , we show that training with SGD on ReLU neural networks gives rise to a natural set of functions for each image that are not perfectly correlated until later in training . Furthermore , we show experimentally that the intersection of paths for different images also changes during the course of training . ... | The authors present empirical results about the correlation between the activations in a neural network across time for a fixed pair of images. They show that for the same pair of images xi and xj, the activations change over time until they settle when the learning rate is sufficiently small. The authors claim that t... | SP:77e60bbe1d3357adcdbe9c340b2f081cbce95090 |
Inferring Offensiveness In Images From Natural Language Supervision | 1 INTRODUCTION . Deep learning models yielded many improvements in several fields . Particularly , transfer learning from models pre-trained on large-scale supervised data has become common practice in many tasks both with and without sufficient data to train deep learning models . Whereas approaches like semisupervise... | The paper constructs a classifier of whether or not an image is offensive. This is operationalized by finding a dataset from the psychology community of a few thousand images and ordinal judgements of 'morality' from a study. Prediction of these judgements is predictably hard, in no small part because the data is small... | SP:244188d1cd932f3a06ce09157bb1206b62becfb0 |
Inferring Offensiveness In Images From Natural Language Supervision | 1 INTRODUCTION . Deep learning models yielded many improvements in several fields . Particularly , transfer learning from models pre-trained on large-scale supervised data has become common practice in many tasks both with and without sufficient data to train deep learning models . Whereas approaches like semisupervise... | This paper described an interesting idea to leverage massively pre-trained models (specifically CLIP-based models) to infer offensiveness in images. The authors first gave detailed literature review regarding a recently raising concern about inappropriate images in computer vision datasets, and also performed several f... | SP:244188d1cd932f3a06ce09157bb1206b62becfb0 |
Domain Invariant Adversarial Learning | 1 INTRODUCTION . Deep learning models have achieved impressive success on a wide range of challenging tasks . However , their performance was shown to be brittle to adversarial examples : small , imperceptible perturbations in the input that drastically alter the classification ( Carlini & Wagner , 2017a ; b ; Goodfell... | The paper describes an adversarial training approach that, in addition to the commonly used robustness loss, requires the network to extract similar representation distributions for clean and attacked data. The proposed method is inspired by domain adaptation approaches that require a model to extract domain invariant/... | SP:064409e33595c152d0f0185b80eb9d533c2d85ce |
Domain Invariant Adversarial Learning | 1 INTRODUCTION . Deep learning models have achieved impressive success on a wide range of challenging tasks . However , their performance was shown to be brittle to adversarial examples : small , imperceptible perturbations in the input that drastically alter the classification ( Carlini & Wagner , 2017a ; b ; Goodfell... | This paper proposes a domain invariant adversarial training (DIAL) method, which learns the feature representation that is both robust and domain invariant. Apart from the label classifier, the model is equipped with a domain classifier that constrains the model not to discriminate between natural examples and adversa... | SP:064409e33595c152d0f0185b80eb9d533c2d85ce |
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning | The lottery ticket hypothesis questions the role of overparameterization in supervised deep learning . But how is the performance of winning lottery tickets affected by the distributional shift inherent to reinforcement learning problems ? In this work , we address this question by comparing sparse agents who have to a... | **Update after reading the other reviews and authors' responses:** Some valid criticism has been raised and addressed by the authors to a reasonable degree (given e.g. the limitations of a conference-format paper). Given all current information I remain in favor of accepting the paper (my score and confidence remains u... | SP:f0163ce76f64a095d124ea46dce4fb2337125157 |
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning | The lottery ticket hypothesis questions the role of overparameterization in supervised deep learning . But how is the performance of winning lottery tickets affected by the distributional shift inherent to reinforcement learning problems ? In this work , we address this question by comparing sparse agents who have to a... | This paper investigates the Lottery Ticket hypothesis in the context of deep RL for identification of sparse task representation in low-dimensional control tasks. This is primarily an empirical investigation where several experiments and consequent analysis reveal what a "winning ticket" means in case of policy models ... | SP:f0163ce76f64a095d124ea46dce4fb2337125157 |
Marginal Tail-Adaptive Normalizing Flows | 1 INTRODUCTION . Heavy-tailed distributions are known to occur in various applications in biology , finance , social sciences , and more . Examples for such observations include the length of protein sequences in genomes ( Koonin et al. , 2006 ) , returns of stocks ( Gabaix et al. , 2003 ) , or the size of cities ( Gab... | This paper focuses on understanding the tail behavior of normalizing flows through a mathematical and statistical way. Motivated by Jaini et al 2020's work on learning long-tailed distribution via triangular flows, this work proves that the marginal tailedness can be controlled by the tailedness of the marginals of the... | SP:863551f0ff2b3fc2b24a545a18b9fb4f5e513a9f |
Marginal Tail-Adaptive Normalizing Flows | 1 INTRODUCTION . Heavy-tailed distributions are known to occur in various applications in biology , finance , social sciences , and more . Examples for such observations include the length of protein sequences in genomes ( Koonin et al. , 2006 ) , returns of stocks ( Gabaix et al. , 2003 ) , or the size of cities ( Gab... | The paper proposes an extension to Tail-adaptive flows for learning the tail behavior of target distributions using normalizing flows. The authors propose to learn the tail behavior by learning flows that match the tail properties of the marginal distributions. They achieve this by using a source distribution consistin... | SP:863551f0ff2b3fc2b24a545a18b9fb4f5e513a9f |
Shift-tolerant Perceptual Similarity Metric | 1 INTRODUCTION . Image similarity measurement is a common task for many computer vision and computer graphics applications . General similarity metrics like PSNR and RMSE , however , do not match the human visual perception well when assessing the similarity between two images . Therefore , many dedicated image similar... | The paper proposes a few modifications to the existing archiectures for perceptual image similarity that would be robust to the tiny shifts in the image. Specifically, the authors conduct an experiemnt that shows that humans mostly are not sensetive to small shifts of 1 or 2 pixels (Table 1), but most state of the art ... | SP:a4f1727f7c84e23cf683786fc4e3be3f066c76d9 |
Shift-tolerant Perceptual Similarity Metric | 1 INTRODUCTION . Image similarity measurement is a common task for many computer vision and computer graphics applications . General similarity metrics like PSNR and RMSE , however , do not match the human visual perception well when assessing the similarity between two images . Therefore , many dedicated image similar... | The authors propose to make perceptual similarity metrics (PSM) invariant to small-shifts (few pixels translation) and still consistent with human judgement. To this end, they use an approach based on network architectures to evaluate which elements (anti-aliasing, pooling, striding, padding, skip connection) can achie... | SP:a4f1727f7c84e23cf683786fc4e3be3f066c76d9 |
An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction | 1 INTRODUCTION . Protein Function Prediction ( PFP ) is one of the key challenges in the post-genomic era ( Zhou et al. , 2019 ; Li et al. , 2018 ) . With large numbers of genomes being sequenced every year , the number of novel proteins being discovered is expanding as well ( Spalević et al. , 2020 ) . On the other s... | This paper presents a model to predict Gene Ontology (GO) term annotations for protein function. The model uses an existing method, SeqVec [2], to encode the protein sequence and a GCN on the Gene Ontology (GO) DAGs to encode the structure of term relationships. Like in DeepGOA[1], the graph is weighted by functions of... | SP:9f1c2067aa3da35a6ab9946ab3bb143b36213da1 |
An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction | 1 INTRODUCTION . Protein Function Prediction ( PFP ) is one of the key challenges in the post-genomic era ( Zhou et al. , 2019 ; Li et al. , 2018 ) . With large numbers of genomes being sequenced every year , the number of novel proteins being discovered is expanding as well ( Spalević et al. , 2020 ) . On the other s... | The paper proposes a method to predict protein functions from Gene Ontology (GO) and protein sequences. The protein sequences are embedded with a pretrained protein language model (SeqVec) and the GO network is modelled with a graph convolutional neural network. The method was benchmarked using CAFA3 competition datase... | SP:9f1c2067aa3da35a6ab9946ab3bb143b36213da1 |
On Locality in Graph Learning via Graph Neural Network | 1 INTRODUCTION . Graph Neural Network ( GNN ) is a family of machine learning ( ML ) models tailored for learning from graph-structured data ( Duvenaud et al. , 2015 ; Li et al. , 2017b ; Gilmer et al. , 2017 ; You et al. , 2019 ) . Recently , great success has been shown using models such as GCN ( Kipf & Welling , 201... | This paper draws connection between performance of GNN and training set coverage in the graph. Specifically, it proposes theoretical study towards structural relation between them in terms of graph distance and empirical classification loss. A set of experiments are designed to validate the theory and assumption on thr... | SP:ddea48ce0c858d47e27f5ab2d31db225b2396479 |
On Locality in Graph Learning via Graph Neural Network | 1 INTRODUCTION . Graph Neural Network ( GNN ) is a family of machine learning ( ML ) models tailored for learning from graph-structured data ( Duvenaud et al. , 2015 ; Li et al. , 2017b ; Gilmer et al. , 2017 ; You et al. , 2019 ) . Recently , great success has been shown using models such as GCN ( Kipf & Welling , 201... | This paper considers the problem of training set selection for graph neural network training. It shows that the generalization from the training to the test set in a well-trained graph neural network is closely tied to the shortest path distance in the graph, which motivates training strategies that "cover" the graph i... | SP:ddea48ce0c858d47e27f5ab2d31db225b2396479 |
Towards Understanding Data Values: Empirical Results on Synthetic Data | 1 INTRODUCTION . Machine learning algorithms stand and fall with the training data . Although the process of data collection and labeling is highly time consuming , the creation of quality training data sets is of paramount importance . However , it turns out that not all data points contribute equally to the quality o... | This paper uses a synthetic two-dimensional dataset to visualize the importance of different data points on machine learning model performance. In particular, they used a multi-layer perceptron as the model, and they used four different schemes by which to measure the importance of individual data points. Not surprisin... | SP:9b43409b72248a99e5271fe974f39077767bd50a |
Towards Understanding Data Values: Empirical Results on Synthetic Data | 1 INTRODUCTION . Machine learning algorithms stand and fall with the training data . Although the process of data collection and labeling is highly time consuming , the creation of quality training data sets is of paramount importance . However , it turns out that not all data points contribute equally to the quality o... | The paper proposes a new method for identifying important points in a dataset given the task of classification. The paper introduces a new valuation function. The paper takes into consideration other methods that deal with the same problem and reevaluated them using the new scoring function. The scoring function focuse... | SP:9b43409b72248a99e5271fe974f39077767bd50a |
Patches Are All You Need? | 1 Introduction For many years , convolutional neural networks have been the dominant architecture for deep learning systems applied to computer vision tasks . But recently , architectures based upon Transformermodels , e.g. , the so-called Vision Transformer architecture ( Dosovitskiy et al. , 2020 ) , have demonstrate... | This work proposed a new design for the image classification task named ConvMixer, which brings the idea from CNN to Visual Transformer. Unlike previous ConvNets, Transformer-based models, and MLP-based models, ConvMixer simply applies depth-wise (with skip connection) and point-wise convolutions on the patches. The ke... | SP:9328554224b618b5c1ab3190a51c86a35e2c7bfd |
Patches Are All You Need? | 1 Introduction For many years , convolutional neural networks have been the dominant architecture for deep learning systems applied to computer vision tasks . But recently , architectures based upon Transformermodels , e.g. , the so-called Vision Transformer architecture ( Dosovitskiy et al. , 2020 ) , have demonstrate... | The paper presents a very simple architecture which consist of patching the input image and then applying a combination of depth-wise and point-wise convolutions. In the paper, authors evaluate the performance of this model when used for image classification. In their main experiment, they train the architecture using ... | SP:9328554224b618b5c1ab3190a51c86a35e2c7bfd |
Chemical-Reaction-Aware Molecule Representation Learning | 1 INTRODUCTION . How to represent molecules is a fundamental and crucial problem in chemistry . Chemists usually use IUPAC nomenclature , molecular formula , structural formula , skeletal formula , etc. , to represent molecules in chemistry literature.1 However , such representations are initially designed for human re... | The paper proposes a molecule representation learning method which is guided by chemical reactions. In particular, it leverages chemical reaction equations by forcing the sum of reactant embeddings and the sum of product embed- dings to be equal for each chemical equation. This idea is simple and useful, sharing the ... | SP:70fa69d4e05f33ab8386117417c229a60e55b658 |
Chemical-Reaction-Aware Molecule Representation Learning | 1 INTRODUCTION . How to represent molecules is a fundamental and crucial problem in chemistry . Chemists usually use IUPAC nomenclature , molecular formula , structural formula , skeletal formula , etc. , to represent molecules in chemistry literature.1 However , such representations are initially designed for human re... | The paper is about learning a vector representation of molecules in a way that the learned representation preserves the equivalence of molecules with respect to chemical reactions. They do so by forcing the sum of reactant embeddings and the sum of product embeddings to be equal for each chemical equation. They have a... | SP:70fa69d4e05f33ab8386117417c229a60e55b658 |
Monotonicity as a requirement and as a regularizer: efficient methods and applications | 1 INTRODUCTION . Highly expressive model classes such as artificial neural networks have achieved impressive prediction performance across a broad range of supervised learning tasks and domains ( Krizhevsky et al. , 2012 ; Graves & Jaitly , 2014 ; Bahdanau et al. , 2014 ) . However , finding predictors attaining low ri... | This paper proposes an incremental improvement to existing methods that encourage monotonicity through a regularization term. The contribution of the paper is about how to sample the data to compute this regularization term, which is an expectation w.r.t. a data distribution. So instead of purely sampling from existing... | SP:615886c264f4481f18aa1a34098c946664a55324 |
Monotonicity as a requirement and as a regularizer: efficient methods and applications | 1 INTRODUCTION . Highly expressive model classes such as artificial neural networks have achieved impressive prediction performance across a broad range of supervised learning tasks and domains ( Krizhevsky et al. , 2012 ; Graves & Jaitly , 2014 ; Bahdanau et al. , 2014 ) . However , finding predictors attaining low ri... | The paper has two main contributions: 1. It takes a known monotonicity regularizer and trains with it using a new distribution that is roughly a mixing of uniform and the training distribution. It shows empirically that using this method increases the "size" of the input region in which the model is monotonic. 2. It... | SP:615886c264f4481f18aa1a34098c946664a55324 |
On Transportation of Mini-batches: A Hierarchical Approach | 1 INTRODUCTION . Optimal transport ( OT ) ( Villani , 2021 ; 2008 ; Peyré et al. , 2019 ) has emerged as an efficient tool in dealing with problems involving probability measures . Under the name of Wasserstein distance , OT has been widely utilized to solve problems such as generative modeling ( Arjovsky et al. , 2017... | Goals: This paper introduces a new optimal transport loss based on a minibatch computation in order to alleviate some weaknesses from the original minibatch OT formulation. The formulation treats minibatches as data and seek to transport the minibatches from the source distribution to the minibatches from the target di... | SP:2c14eabf1f6b4c828ab8c59c608421860fc9e7e0 |
On Transportation of Mini-batches: A Hierarchical Approach | 1 INTRODUCTION . Optimal transport ( OT ) ( Villani , 2021 ; 2008 ; Peyré et al. , 2019 ) has emerged as an efficient tool in dealing with problems involving probability measures . Under the name of Wasserstein distance , OT has been widely utilized to solve problems such as generative modeling ( Arjovsky et al. , 2017... | This paper proposed Batch of Mini-batches Optimal Transport (BoMb-OT) method, which finds the optimal coupling between mini-batches in mini-batch optimal transport (m-OT), which is achieved by solving another OT problem over the mini-batches. The authors claimed that doing this will capture the relation between differ... | SP:2c14eabf1f6b4c828ab8c59c608421860fc9e7e0 |
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning | 1 INTRODUCTION . In the light of the importance of personal data and the recent strict privacy regulations , e.g . the General Data Protection Regulation ( GDPR ) of the European Union ( Voigt & Von dem Bussche , 2017 ; Wolters , 2017 ; Politou et al. , 2018 ) , there is now a great amount of risk , responsibility ( Ed... | The paper proposes a new method where local workers will drop part of their model using a shared dropout probability received from the server at each communication round. The dropout probabilities are computed by solving an optimization to promote similarity across agent's update. Compared with popular baselines in fed... | SP:48c5c18dac59913411633150b5f40fbcd1647d1e |
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning | 1 INTRODUCTION . In the light of the importance of personal data and the recent strict privacy regulations , e.g . the General Data Protection Regulation ( GDPR ) of the European Union ( Voigt & Von dem Bussche , 2017 ; Wolters , 2017 ; Politou et al. , 2018 ) , there is now a great amount of risk , responsibility ( Ed... | The authors propose a new method of coordinated, per client and weight dropout for federated learning. The intuition behind the method is to increase the dropout probability (probability of having a weight set to zero) for weights where different clients often have opposite parity gradients (as when those gradients are... | SP:48c5c18dac59913411633150b5f40fbcd1647d1e |
Programmatic Reinforcement Learning without Oracles | 1 INTRODUCTION . A growing body of research has explored programs in a domain-specific programming language as a new RL policy representation that intentionally encourages policy interpretability . Yet , learning a policy as a high-level program in structured representations is challenging . This is because algorithms ... | This paper addresses the problem of the low efficiency of program search guided by a pre-trained oracle or on discrete and non-differentiable architecture space. To this end, the paper proposes a framework that performs program architecture search on top of a differentiable relaxation of the architecture space. This al... | SP:633cf2b404a8d76c5f4fc2e2c88546b9a35a7688 |
Programmatic Reinforcement Learning without Oracles | 1 INTRODUCTION . A growing body of research has explored programs in a domain-specific programming language as a new RL policy representation that intentionally encourages policy interpretability . Yet , learning a policy as a high-level program in structured representations is challenging . This is because algorithms ... | This paper presents a novel method for synthesizing programmatic policies. The code idea of the method is to define a relaxed and differentiable version of the domain-specific language (DSL) used to encode the programmatic policies. The program space the DSL induces can be described as a program of the DSL itself. Si... | SP:633cf2b404a8d76c5f4fc2e2c88546b9a35a7688 |
Divisive Feature Normalization Improves Image Recognition Performance in AlexNet | 1 INTRODUCTION . Neural networks ( NN ’ s ) in general and convolutional NN ’ s ( CNN ’ s ) in particular were originally inspired by the brain . However , only the barest sketch of brain function has been incorporated into NN ’ s . Conversely , studies of brain-like function in NN ’ s have only begun to impact neurosc... | The authors study the role of the biologically realistic divisive normalization computation in the context of deep learning models trained to perform image classification tasks. The authors compare divisive normalization (scaling neuronal response by exponentially weighted sum of its neighbors) with those normalization... | SP:0f44e739c4536b6b955b11f47a2d16b2326926ce |
Divisive Feature Normalization Improves Image Recognition Performance in AlexNet | 1 INTRODUCTION . Neural networks ( NN ’ s ) in general and convolutional NN ’ s ( CNN ’ s ) in particular were originally inspired by the brain . However , only the barest sketch of brain function has been incorporated into NN ’ s . Conversely , studies of brain-like function in NN ’ s have only begun to impact neurosc... | The authors study the effect of divisive normalization on AlexNet. They show that, when combined with standard normalization schemes, it increases performance. They also investigate the filter shapes, manifold capacity and (adversarial) robustness of the learned representations. Following the authors' response I have ... | SP:0f44e739c4536b6b955b11f47a2d16b2326926ce |
Transferable Adversarial Attack based on Integrated Gradients | 1 INTRODUCTION . Adversarial example , which can mislead deep networks is one of the major obstacles for applying deep learning on security-sensitive applications ( Szegedy et al. , 2014 ) . Researchers found that some adversarial examples co-exist in models with different architectures and parameters ( Papernot et al.... | In practice, adversarial examples are generated in three ways, i) solving a standard optimisation problem, ii) leveraging the salient regions of an image, or iii) smoothing the decision surfaces. The authors propose a simple technique named Transferable Attack based on Integrated Gradients (TAIG) that combines all thes... | SP:163b6f1e8787eb5d48bf477b3ef3c0a00d41a937 |
Transferable Adversarial Attack based on Integrated Gradients | 1 INTRODUCTION . Adversarial example , which can mislead deep networks is one of the major obstacles for applying deep learning on security-sensitive applications ( Szegedy et al. , 2014 ) . Researchers found that some adversarial examples co-exist in models with different architectures and parameters ( Papernot et al.... | Two methods are proposed in this paper. They are Transferable Attack using Integrated Gradients on Straight-line Path (TAIG-S) and Transferable Attack using Integrated Gradients on Random Piecewise Linear Path (TAIG-R). Compared with typical gradient-based attack methods, the TAIG-S uses integrated gradients to update ... | SP:163b6f1e8787eb5d48bf477b3ef3c0a00d41a937 |
The Information Geometry of Unsupervised Reinforcement Learning | 1 INTRODUCTION . The high sample complexity of reinforcement learning ( RL ) algorithms has prompted a large body of prior work to study pretraining of RL agents . During the pretraining stage , the agent collects unsupervised experience from the environment that is not labeled with any rewards . Prior methods have use... | This paper is trying to analyze whether unsupervised skill discovery is useful for more easily solving any possible downstream tasks in an MDP. It does so by adapting the idea of the value function polytope to a state visitation distribution polytope. It also specifies possible reward functions in this geometric settin... | SP:cd0d3d64c3bfae598cd59fc7597531d30251dd54 |
The Information Geometry of Unsupervised Reinforcement Learning | 1 INTRODUCTION . The high sample complexity of reinforcement learning ( RL ) algorithms has prompted a large body of prior work to study pretraining of RL agents . During the pretraining stage , the agent collects unsupervised experience from the environment that is not labeled with any rewards . Prior methods have use... | The paper treats the problem of unsupervised RL, which it defines as the problem of pretraining a system, without having access to a reward function, to learn a collection of policies, that are labeled skills. The idea is that when the reward function is presented, the target policy can be assembled as a combination or... | SP:cd0d3d64c3bfae598cd59fc7597531d30251dd54 |
Neural Relational Inference with Node-Specific Information | 1 INTRODUCTION . Our world includes many different types of systems that involve multiple entities interacting with each other , from biology to sports , from social media to driving situations . Modelling the behaviour of such dynamical systems is a challenging task , which requires uncovering different types of inter... | The paper introduces the concept of node-specific information (NSI) to model that nodes in a graph may have private information that other nodes cannot have access to. The paper uses Neural Relation Inference (NRI), a framework published in 2018 based on variational inference, to uncover the hidden relations of nodes i... | SP:7f6ac27e9ec6db3f4860406263b59f88c2cfeacc |
Neural Relational Inference with Node-Specific Information | 1 INTRODUCTION . Our world includes many different types of systems that involve multiple entities interacting with each other , from biology to sports , from social media to driving situations . Modelling the behaviour of such dynamical systems is a challenging task , which requires uncovering different types of inter... | **Summary**: This paper introduces a neural relational inference model that makes use of the hidden features of each node in a variational inference framework. Specifically, the hidden/individual information is modeled as private node in the graph. Importantly, the task assumption made by the authors is that these indi... | SP:7f6ac27e9ec6db3f4860406263b59f88c2cfeacc |
SCformer: Segment Correlation Transformer for Long Sequence Time Series Forecasting | 1 INTRODUCTION . Time series forecasting has always been a classic machine learning problem . It is widely used in various fields that are closely related to our lives , e.g. , production planning , financial investment , traffic management , and electricity management . In many cases , we need to predict the future va... | This paper presents a Transformer-based model called SCformer to perform long sequence time series forecasting. The key idea is to replace the canonical self-attention with efficient segment correlation attention (SCAttention) mechanism to capture long short-term dependencies. Experiment results on several datasets sho... | SP:955ba7c70fa3640478b5ae1bb562025a1cb14a04 |
SCformer: Segment Correlation Transformer for Long Sequence Time Series Forecasting | 1 INTRODUCTION . Time series forecasting has always been a classic machine learning problem . It is widely used in various fields that are closely related to our lives , e.g. , production planning , financial investment , traffic management , and electricity management . In many cases , we need to predict the future va... | This paper introduces a SCFORMER, which replaces the canonical attention in the Transformer with the segment correlation attention. The motivation of using the segment correlation is to reduce the memory usage of the scale-dot product attention of the Transformer. To further improve the performance, the paper proposes ... | SP:955ba7c70fa3640478b5ae1bb562025a1cb14a04 |
Pruning Compact ConvNets For Efficient Inference | 1 INTRODUCTION . Neural networks frequently suffer from the problem of over-parameterization , such that the model can be compressed by a large factor to drastically reduce memory footprint , computation as well as energy consumption while maintaining similar performance . This is especially pronounced for models for c... | This paper applies conventional pruning-and-finetuning techniques to further compress the networks searched by NAS. The experiments and evaluations are based on the family of FBNetV3. The authors show that by pruning large FBNetV3 model to small one, the accuracy of pruned model may be slightly better than the original... | SP:070b00b3bd28545b1bbdf3f6884e748756fb3101 |
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