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Permutation Compressors for Provably Faster Distributed Nonconvex Optimization | √ n ) ( resp . O ( 1 + d/ √ n ) ) improvement in the theoretical communication complexity of MARINA in the low Hessian variance regime when d ≥ n ( resp . d ≤ n ) , where n is the number of workers and d is the number of parameters describing the model we are learning . We corroborate our theoretical results with caref... | This paper investigates a permutation based sparsification technique for distributed optimization, and prove that, under a mildly tighter condition than L-smoothness, it can achieve better communication complexity than the existing random sparsification technique. The method is largely under the larger construct of MAR... | SP:718e14c8917bcbc7ad2fd8af376d220a77886e92 |
An evaluation of quality and robustness of smoothed explanations | 1 INTRODUCTION . Explanation methods attribute a numerical value to each data feature in order to quantify its relative importance towards the model ’ s prediction . Such attributions help to better understand and trust complex models like deep neural networks ( DNNs ) . In safety-critical tasks , such an understanding... | This paper evaluates the quality and robustness of explanations of three post-hoc smoothing approaches (Smooth Gradient, Uniform Gradient, B-smoothing), and two ad-hoc smoothing approaches (CURE, Adv). It evaluates the quality of explanations based on the model parameter sensitivity, class sensitivity, sparseness, infi... | SP:a05f50fa43df843af97048eda07c97447deda6ed |
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression | 1 INTRODUCTION . As one of the efficient compression methods , pruning reduces the number of parameters by replacing model parameters of low importance with zeros ( LeCun et al. , 1990 ) . Since magnitude-based pruning has shown that pruning can be conducted with low computational complexity ( Han et al. , 2015 ) , var... | This paper tackles an interesting and important question which is how can we make the pruned network regular such that it can run quickly on GPU / common hardwares. The problem essentially originates from the fact that typical computational hardware can be slow when accessing in-contiguous memory address. This paper pr... | SP:121b487452295880fb4ad01ea6236b79d79e6282 |
Unit Ball Model for Embedding Hierarchical Structures in the Complex Hyperbolic Space | 1 INTRODUCTION . Representation learning of data with hierarchical structures is an important machine learning task with many applications , such as taxonomy induction ( Fu et al. , 2014 ) and hypernymy detection ( Shwartz et al. , 2016 ) . In recent years , the hyperbolic embeddings ( Nickel and Kiela , 2017 ; 2018 ) ... | The paper introduces an extension of real hyperbolic embeddings to the complex hyperbolic space [A]. The exploited geometry is an extension of the Poincaré ball that contains complex vectors (instead of real vectors) whose norm is smaller than 1. The resulting manifold is of nonconstant negative curvature, which the au... | SP:54f48b0f37ee22d8027ab01da5ea6801520d9dc3 |
iPrune: A Magnitude Based Unstructured Pruning Method for Efficient Binary Networks in Hardware | 1 INTRODUCTION . AI has become ubiquitous in our daily lives , be it the use of search engines to find information or automatically tagging photos on social media . While the predictive accuracy of these models have grown , the size and computation requirements have grown by several orders of magnitude or more , making... | This paper combines two existing methods (i.e., binarization and pruning) to reduce both computational complexity and memory requirements of deep neural networks (DNNs). Binarryconnect method was exploited to binarize DNNs whereas neuron-wise magnitude pruning was used to remove unnecessary input activations. It was th... | SP:b3f80afc622b0491c582756b39d9497dd95df96c |
The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program | 1 INTRODUCTION . Neural networks ( NNs ) exhibit remarkable empirical performance in various machine learning tasks . However , a full characterization of the optimization and generalization properties of NNs is far from complete . Non-linear operations inherent to the structure of NNs , over-parameterization and the a... | The paper studies the subgradient flows when training a two-layer ReLU neural network. To this end, the non-convex max-margin problem is reformulated as a convex optimization problem. The authors then analyze the dual extreme points of the convex formulation and show the implicit regularization of unregularized gradien... | SP:4213ada856e1c6bfe4d1b85ca187db8a5d6aadfd |
Quasi-potential theory for escape problem: Quantitative sharpness effect on SGD's escape from local minima | 1 INTRODUCTION . In recent years , the successes of deep learning have been a major driving force of machine learning development ( LeCun , 2019 ) . Owing to its strong generalization capability , deep learning has diverged into a wide range of domains , such as computer vision ( Krizhevsky et al. , 2012 ) , speech rec... | This paper uses the quasi potential theory to formalize the escape behavior of SGD happening while training a deep neural network. The quasi potential is defined based on the steepness of a trajectory and is the smallest steepness to go from the minimizer to another point. Utilizing this they analyze both continuous an... | SP:49228d3db309a75dcfb9973e0ee4795e75f49a3f |
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization of Lazily-Aggregated Gradients | 1 INTRODUCTION . With the deployment of ubiquitous sensing and computing devices , the Internet of things ( IoT ) as well as many other distributed systems have gradually grown from concept to reality , bringing dramatic convenience to people ’ s daily life ( Du et al. , 2020 ; Liu et al. , 2020a ; Hard et al. , 2018 )... | The paper proposes combining two orthogonal algorithms -- the lazily aggregated gradient(LAQ)method and adaptive quantization (AdaQuantFL)-- to reduce communication complexity in federated learning. In particular, while LAQ focuses on the frequency of gradient updates in each iteration, AdaQuantFL focuses on the adapti... | SP:64968c4bea37e7f03d94c474aef30bd767312015 |
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection? | 1 INTRODUCTION . Outlier detection is the process of identifying unusual data points , or collections of data points within a dataset . Within different contexts , these outliers can take various forms , for example : • Credit card fraud detection : Unusual spending . • Internet traffic monitoring : Unusual surges in t... | This paper studies time series outlier detection. The authors propose to compare recurrent and static outlier detection methodologies on multiple synthetic and real-world temporal datasets. Additionally, they present an extension to the LSTM outlier detection using an attention-based approach. The analysis suggests tha... | SP:50baa34c330b54a80693307a90da72de6bccfa68 |
What classifiers know what they don't know? | 1 INTRODUCTION I don ’ t think I ’ ve ever seen anything quite like this before —HAL 9000 in 2001 : A Space Odyssey Deep image classifiers exceed at discriminating the set of in-domain classes observed during training . However , when confronting test examples from unseen out-domain classes , these classifiers can only... | This paper proposes the UIMNET benchmark for uncertainty estimation. This benchmark includes (1) "ImageNot", a remix of ImageNet using hierarchical clustering of pairwise distances between features (2) framework for evaluating uncertainty estimation, including a suite of algorithms, metrics, and ablations studies. The ... | SP:9d80245615b9fce3b978ffbdbafa6a33e9edee4a |
Generating Realistic 3D Molecules with an Equivariant Conditional Likelihood Model | 1 INTRODUCTION . Identifying molecules with desirable characteristics is of fundamental importance in many fields , including drug discovery . The astronomically large number of possible drug-like compounds , however , makes an exhaustive experimental or virtual screening intractable ( Polishchuk et al. , 2013 ) . As a... | This paper proposes a generative model to sample a new molecule with its 3d coordinates. The generative process is divided into three different steps: 1) the atom network generates a new atom 2) the edge network connects the new atom to the existing atoms 3) the distance and angle network generates the pairwise distanc... | SP:0fe019136bdbcb8818039e156c3ef54c50420e72 |
Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning | 1 INTRODUCTION . With the ubiquitous adoption of machine learning algorithms to facilitate decision making , algorithm fairness has attracted increasingly more attentions , in the areas such as recommendation system Ge et al . ( 2021 ) ; Zhu et al . ( 2018 ) ; Burke ( 2017 ) ; Yao & Huang ( 2017 ) , natural language pr... | This paper aims to improve path-specific causal fairness by removing unfair causal pathways. Basically, the framework leverages the existing work by Kyono et al. and adds additional regularization terms. A big advantage of the proposed method is that it can handle the case where sensitive attribute is not root node. Ba... | SP:6c884b1082787c96858a266006b2f3c59f0e1e14 |
Coarformer: Transformer for large graph via graph coarsening | 1 INTRODUCTION . In recent years , the Transformer architecture ( Vaswani et al. , 2017 ) has been derived into several variants , e.g. , BERT ( Devlin et al. , 2019 ) and ViT ( Dosovitskiy et al. , 2021 ) , which achieve unprecedented successes in natural language processing ( NLP ) and computer vision ( CV ) , respec... | This paper proposes to use the transformer architecture on the coarse graph obtained from the graph coarsening algorithm. To be more specific, * Since directly using the transformer architecture to the large-scale graph is computationally prohibitive, the authors use the existing graph coarsening algorithms to the lar... | SP:34963abebc7bff45ac1b8fae499ceea8900c2852 |
Self-supervised Learning for Sequential Recommendation with Model Augmentation | The sequential recommendation aims at predicting the next items in user behaviors , which can be solved by characterizing item relationships in sequences . Due to the data sparsity and noise issues in sequences , a new self-supervised learning ( SSL ) paradigm is proposed to improve the performance , which employs cont... | This paper a new self-supervised learning (SSL) paradigm for sequence recommendation by contrastive learning between positive and negative views of sequences based on model augmentation. The model augmentation methods includes neuron masking, layer dropping and encoder complementing. The proposed algorithm is evaluat... | SP:3bee8aa34c433357c9487dc3ec37837d18f0c143 |
On the Practicality of Deterministic Epistemic Uncertainty | 1 INTRODUCTION . Despite the dramatic enhancement of predictive performance of deep learning ( DL ) , its adoption remains limited due to unpredictable failure on out-of-distribution ( OOD ) samples ( 1 ; 2 ) and adversarial attacks ( 3 ) . Uncertainty estimation techniques aim at bridging this gap by providing accurat... | This paper mainly summarizes and evaluates the existing methods for estimating epistemic uncertainty through a single pass of the neural networks. The authors categorize deterministic uncertainty methods (DUMs) based on how latent representation is learned under regularization and how uncertainty is quantified. For eva... | SP:67f90b5a1664683ab391a395416d4fd1f8b5599e |
Convergence of Generalized Belief Propagation Algorithm on Graphs with Motifs | 1 INTRODUCTION . Undirected graphical models , also known as Markov Random Fields ( MRF ) , provide a framework for modeling high dimensional distributions with dependent variables . Ising models are a special class of discrete pairwise graphical models originated from statistical physics . Ising models have numerous a... | The author considers a special class of attractive models and extends the convergence results of [Koehler]. In general, the convergence behavior of belief propagation algorithm is hard to be understood, however, attractive models are special class of models known to have simple optimization loss landscape. Therefore, t... | SP:d8bf2a93ae4915034af540e2c8ee69e3495f2419 |
Quantifying the Controllability of Coarsely Characterized Networked Dynamical Systems | 1 INTRODUCTION . In this paper we study controllability for networked dynamical systems when our knowledge of system structure is limited to coarse summaries . We are motivated by myriad real-world settings where system identification must be performed based upon measurements taken by low-resolution instruments unable ... | The paper studies the problem of controlling the dynamics of a networked dynamical system, under partial observations. Such systems arise in a number of applications, and therefore the control problem is important and well motivated. The authors consider a reduced order system from coarse data, and derive bounds on the... | SP:64d7728c0dc05f53fa69d32459083559d839f017 |
Design and Evaluation for Robust Continual Learning | 1 INTRODUCTION . Continual learning is the ability to learn from new non-stationary data without catastrophic forgetting ( McCloskey & Cohen , 1989 ; Goodfellow et al. , 2013 ; Kemker et al. , 2018 ) previously learned experiences ( Thrun & Mitchell , 1995 ; Hsu et al. , 2018 ; Parisi et al. , 2019 ) . Unlike transfer ... | This work proposes 6 rules for continual learning experiments. Specifically, they propose to - 1) test for extreme continual learning settings, - 2) report random guess performance - 3) evaluate naive/simple methods - 4) revisit old data - 5) test for different task orders and - 6) define performance that derive ... | SP:0d39f7f68ef4bedb277b036022a3cd95eb4f4d5d |
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting | Graph Neural Networks ( GNNs ) are proven to be a powerful machinery for learning complex dependencies in multivariate spatio-temporal processes . However , most existing GNNs have inherently static architectures , and as a result , do not explicitly account for time dependencies of the encoded knowledge and are limite... | This paper proposes the use of multi-parameter persistence (an emergent research topic in topological data analysis) to capture latent time-conditioned relations among nodes in a GNN. To do so, the authors introduce a dynamic Euler-Poincaré surface as a new multi-parameter persistence summary, and prove its stability a... | SP:40cc8c0b50b53496cf272ac1f65794c41396a69c |
MANDERA: Malicious Node Detection in Federated Learning via Ranking | 1 INTRODUCTION . Federated learning ( FL ) has observed a steady rise in use across a plethora of applications . FL departs from conventional centralized learning by allowing multiple participating nodes to learn on a local collection of training data , before each respective node ’ s updates are sent to a global coord... | The paper focuses on Byzantine defense through malicious node detection in a Federated Learning setting. Namely, by ranking the gradients and then computing the mean/SD, the paper shows that the malicious and benign clients will cluster separately. Assuming that the number of malicious clients is fewer than the number ... | SP:8f93bbdf09dfedfb72be6bee9d42932c66fb8535 |
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism | Offline reinforcement learning , which seeks to utilize offline/historical data to optimize sequential decision-making strategies , has gained surging prominence in recent studies . Due to the advantage that appropriate function approximators can help mitigate the sample complexity burden in modern reinforcement learni... | This paper proposes a re-weighting of the Bellman update for offline RL, using the variance term, that leads to an improved bound for offline RL. The paper is well written and easy to follow, with the key improvement (theoretically) easy to see. The core idea of the algorithm is a simple trick to re-weight the update a... | SP:22e879b356b61d0358a66e9275ac841c4ec3e52c |
Deep convolutional recurrent neural network for short-interval EEG motor imagery classification | 1 INTRODUCTION . The use of Deep Neural Networks ( DNN ) for the challenging task of interpreting and classifying the noisy and idiosyncratic signals produced by electroencephalography ( EEG ) is beginning to show promising results . Some of the best performing models , such as EEGNet ( Lawhern et al. , 2018 ) and Shal... | The authors introduce a deep learning approach for short-time motor imagery classification using EEG data. Conventional CNN and RNN - GRU layers are used. Remarkably, a data augmentation strategy and a class-activation mapping approach are presented. Overall, the idea is interesting, but the paper presentation, the ma... | SP:063eee21dbfecedd3d8d0319f1a40d8debcb3c28 |
Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled Data | 1 INTRODUCTION . An input of a supervised binary classifier consists of two sets of examples : positive and negative . However , the access to clean samples from both classes can be obstructed in many realistic scenarios . A particularly well-studied restriction is the absence of clean negative examples . One of the ap... | This work empirically investigates using the PU loss in Positive-Unlabeled learning in the one-class classification task. The authors propose several PU modifications of the original classification algorithms that can leverage the unlabeled data. Experiments show improvements after the modifications. | SP:a12475329ccc3658d968469809564333113684dd |
Degradation Attacks on Certifiably Robust Neural Networks | 1 INTRODUCTION . An adversarial example for a neural classifier is the result of applying small modifications to a correctly classified valid input such that the modified input is classified incorrectly . For neural classifiers trained in a standard manner , it has been shown that adversarial examples are rampant ( Sze... | This paper proposes a degradation attack on certifiable defenses GloRo and randomized smoothing aiming to find correct inputs within robustness region but hard to certify, causing rejections. The proposed attack is simple which is just a smoothed variance of PGD attack. Experimental results show that the two evaluated ... | SP:d962a80354f15ca16911902e946a774d85f8b8d8 |
On the Existence of Universal Lottery Tickets | 1 INTRODUCTION . Deep learning has achieved major breakthroughs in a variety of tasks ( LeCun et al. , 1990 ; Schmidhuber , 2015 ) , yet , it comes at a considerable computational cost ( Sharir et al. , 2020 ) , which is exaggerated by the recent trend towards ever wider and deeper neural network architectures . Reduci... | This paper proves that large enough randomly initialized ReLU networks can contain subnetworks, which can act as universal lottery tickets, that is, only by tuning the last layer of these subnetworks, they can approximate any (reasonably well-behaved) function. The paper does this by showing that large enough randomly ... | SP:7280861bf3704c3cc2fbecb601956c716dae3eaf |
LatTe Flows: Latent Temporal Flows for Multivariate Sequence Analysis | 1 INTRODUCTION . One of the core objectives in machine learning research is to build models that accurately capture and explain complex structures in real-world systems . Much of unsupervised learning is driven by probabilistic generative modeling , which is notoriously difficult in high dimensions as a consequence of ... | The authors propose a novel approach that scales well for multivariate sequence forecasting tasks. They offer to use conditional normalizing flow to capture the sequence dynamics in the latent space and then use auto-regressive architecture to decode the signal in the original space. The authors clearly explain why the... | SP:13234b1c2a77b736e7f6187261c93815d2153663 |
Neural Spectral Marked Point Processes | 1 INTRODUCTION . Event sequence data are ubiquitous in our daily life , ranging from traffic incidents , 911 calls , social media posts , earthquake catalog data , and COVID-19 data ( see , e.g. , Bertozzi et al . ( 2020 ) ) . Such data consist of a sequence of events indicating when and where each event occurred , wit... | This paper propose a point process with a non-stationary kernel to model complex event data. The kernel represented by its finite rank decomposition and the basis functions (feature functions) are models using a neural network architecture. To learn the model parameters they used stochastic gradient to maximize the res... | SP:85d7fc981d3c7f1ca215a976c476cc92c1be1e4e |
Momentum Doesn't Change The Implicit Bias | 1 INTRODUCTION . It is widely believed that the optimizers have implicit bias in terms of selecting output parameters among all the local minima on the landscape ( Neyshabur et al. , 2015 ; Keskar et al. , 2017 ; Wilson et al. , 2017 ) . It is shown in the analysis of Adaboost that the coordinate descent would converge... | The authors study the convergence of stochastic gradient descent momentum (SGDM) in a problem with of binary classification with separable data. Interestingly, the authors observed that SGDM and GD converge to the same solution (i.e. the max margin solution) with the same convergence rate O(1/t). The paper proposed a... | SP:067d1acf136e163bf7a3f43bb8b945c73feef657 |
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks | 1 INTRODUCTION . Adversarial attacks aim to find adversarial examples ( Szegedy et al. , 2013 ) , which are close to benign inputs in certain distance metrics yet trigger wrong behavior of neural networks ( Carlini & Wagner , 2017 ; Madry et al. , 2018 ; Athalye et al. , 2018 ; Croce & Hein , 2020b ) . Adversarial atta... | This paper proposes a branch-and-bound attack (BaB-Attack) to solve hard instances efficiently, where none of the existing adversarial attacks can succeed. Specifically, the BaB-Attack utilizes the bound propagation-based neural network verifiers on GPUs to rapidly evaluate a large number of searching regions, builds a... | SP:ab9546306ff0171715cc15c1003afd07b76e3d15 |
Self Reward Design with Fine-grained Interpretability | 1 INTRODUCTION . Reinforcement Learning ( RL ) and Deep Neural Network ( DNN ) have recently been integrated to solve problems with remarkable performance . The deep reinforcement learning greatly improves the state-of-the-art of control and , in the words of Sutton & Barto ( 2018 ) , learning from interaction . Among ... | This paper proposes using individually designed, interpretable neural networks to solve a given task. Each neuron in the network is hand-designed to serve a specific task-dependent purpose. These hand-designed solutions are then optimized using environment interactions. The neuron level design of the network is used to... | SP:a435b0f719cd07f80beba1772966918c6114b201 |
NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks | 1 INTRODUCTION . It is crucial in many applications of modern machine learning methods to complement the prediction with some sort of a “ confidence ” score . In particular , deep neural network models , which usually achieve state-of-the-art results in various tasks , are notorious for providing overconfident predicti... | This paper proposes a nonparametric uncertainty quantification method for deep neural networks. Using a kernel-based estimator of the conditional density (i.e., the predictive distribution $p(y|x)$) in the feature space of a pre-trained neural network, the epistemic and the aleatoric uncertainty can be obtained separat... | SP:df1f6865ae290546a4d51d40f6ad3b2bfc900d7e |
Automated Channel Pruning with Learned Importance | Neural network pruning allows for significant reduction of model size and latency . How-1 ever , most of the current network pruning methods do not consider channel interdepen-2 dencies and a lot of manual adjustments are required before they can be applied to new3 network architectures . Moreover , these algorithms ar... | This paper proposes a neural network pruning and fine-tuning framework for model compression. It can automatically prune the channels by learning the channel importance. The contributions are: 1) A new pruning scheme is proposed by learning the channel importance; 2) The pruning logic is introduced in the pruning schem... | SP:ef83c8218b8940bfb37409d1e4ed2264f131afd2 |
Compressing Transformer-Based Sequence to Sequence Models With Pre-trained Autoencoders for Text Summarization | 1 INTRODUCTION . It is safe to say that the combination of Transformer ( Vaswani et al. , 2017 ) architecture , and transfer learning concept dramatically modified the landscape of Natural Language Processing ( NLP ) . Introduction of large-scale pre-trained language models like BERT ( Devlin et al. , 2018 ) , GPT-2 ( ... | The paper proposes a new autoencoder-based seq2seq model for text summarization tasks. The paper tries to find the best trade-off between compression ratio and model performance. The paper conducts extensive experiments by evaluating the loss of accuracy with ROUGE. | SP:d2640ee89bae300f4dc4c6e3225698dc2077b5f4 |
Invariant Learning with Partial Group Labels | 1 INTRODUCTION . Neural networks being overly biased to certain groups of the data is an increasing concern within the machine learning community [ 1 ] . A primary cause for bias against specific groups is the presence of extraneous attributes in the datasets that wrongly direct the model responses [ 38 ] . Such extran... | The paper proposes a new variant of DRO, called Worst-Off DRO, to address a ubiquitous real-world setting where group labels are only partially available over the training set. The core idea is to introduce a nested optimization to maximize the worst-off group assignment over the entire training set, given some sensibl... | SP:2455a45bf2ad92e7ccbc2e5eaf0482bcfafab7c2 |
Deep banach space kernels | 1 INTRODUCTION . In recent years , promising new variants of kernel learning methods , namely deep ker-nel learning and multi-layer-MKL ( MLMKL ) algorithms have been developed . These concatenated kernel learning approach includes , see e.g . Cho & Saul ( 2009 ) ; Damianou & Lawrence ( 2013 ) ; Zhuang et al . ( 2011 )... | The paper presents a means of constructing deep (concatenated) reproducing kernel Banach space kernels. The main contribution appears to be a representor theory for concatenated Banach space kernels. Some experimental results are provided to accompany this result. | SP:e61b82a7bc8c843fe38c6524f60214f39dc1b014 |
Coherence-based Label Propagation over Time Series for Accelerated Active Learning | 1 INTRODUCTION . A time series is a sequence of data points at successive timestamps . Supervised learning ( e.g. , classification ) with a time series requires the label of every data point , but unfortunately labels are often missing and hard to obtain due to lack of domain-specific knowledge ( Shen et al. , 2018 ; M... | The goal of the paper is to improve active learning for time-series. The goal is to learn a function from x to y, with data coming from a time series (x_t) and (y_t). It is assumed that (y_t) is piecewise constant. At the beginning no (y_t) are known, but query can be made in a active learning framework. This paper t... | SP:3a65cc77a884203b73add6419e8a5ee63a906b7f |
A Variance Principle Explains why Dropout Finds Flatter Minima | 1 INTRODUCTION . Dropout is used with gradient-descent-based algorithms for training DNNs ( Hinton et al. , 2012 ; Srivastava et al. , 2014 ) . During training , the output of each neuron is multiplied with a random variable with probability p as one and 1−p as zero . Note that p is called dropout rate , and every time... | This paper provides an empirical study on how the dropout can lead to minima within a flatter landscape (hence a better generalization performance). Based on the definition of minimizer flatness (i.e., eq. 5) and random trajectory data (parameters from a optimization path and gradients when loss is stably small), the a... | SP:07fcfaa31ed0de5c83b75145f6648a7b0dffe90a |
NASPY: Automated Extraction of Automated Machine Learning Models | 1 INTRODUCTION . Recently Automated Machine Learning ( AutoML ) has attracted lots of attention from the machine learning community , as it can significantly simplify the development of machine learning pipelines with high efficiency and automation . One of the most popular AutoML techniques is Neural Architecture Sear... | This paper presents an end-to-end adversarial framework to extract the network architecture obtained from Neural Architecture Search (NAS), named NASPY. Previous works focus on extracting conventional ConvNets with simple operations, while requiring heavy human analysis. In contrast, NASPY introduces a seq2seq network ... | SP:9083f2ca2c43a7037bbc444f730e48c190e1c8c0 |
Causal Contextual Bandits with Targeted Interventions | 1 INTRODUCTION . Contextual bandits have been used as natural frameworks to model interactive decision making scenarios such as recommendation systems ( Liu et al. , 2018 ) , marketing campaign allocation ( Sawant et al. , 2018 ) and more ( Bouneffouf & Rish , 2019 ) . In this framework , the learning agent repeatedly ... | The paper studies a contextual bandit setting with two unique features: (1) the learning agent has ability to perform targeted interventions during the learning phase (ability to select target sub-populations or context) and (2) it also has access to and integrates casual information in the setting. The key motivation ... | SP:deb40959d914e612a47c564c011dae2771964dce |
Ensemble Kalman Filter (EnKF) for Reinforcement Learning (RL) | 1 INTRODUCTION . This paper is concerned with the problem of reinforcement learning ( RL ) in continuous-time and continuous ( Euclidean ) state-space settings . A special case is the linear quadratic Gaussian ( LQG ) problem where the dynamic model is a linear system , the cost terms are quadratic , and the distributi... | The paper is about designing a simulation-based ensemble Kalman filter algorithm for learning the optimal control policy for the Linear Quadratic Gaussian (LQG) control problem. The main contribution is that the paper extends the existing ensemble Kalman filter algorithm for filter (state estimation) design to the opti... | SP:ebc788d6c2ddc10749aac5fc1a5353d68d36fbeb |
Cluster-based Feature Importance Learning for Electronic Health Record Time-series | 1 INTRODUCTION . Chronic conditions such as Chronic Obstructive Pulmonary Disease ( COPD ) and Cardiovascular Disease ( CVD ) describe a broad spectrum of medical ailments , and affect a significant percentage of the overall population ( Adeloye et al. , 2015 ) . Such diseases are characterized by the existence of mult... | The paper presents a way to predict outcomes of patients using EHR records. The authors propose a phenotyping model which clusters similar patients based on their outcome distribution. They also introduce feature-time relevance map which helps explain for a particular patient the feature-time combinations which are imp... | SP:4ecd135494e61651f82e86e1adac4ce2b2f79a1c |
Coresets for Kernel Clustering | 1 INTRODUCTION . We design the first coresets , and consequently new efficient algorithms , for kernel k-MEANS and related problems , like its generalization kernel ( k , z ) -CLUSTERING , under general kernels . The kMEANS problem has proved to be fundamental for unsupervised learning in numerous application domains .... | The paper claims to present the first coreset for kernel k-means and other (k,z) kernel clusterings given oracle access to the kernel. The size of coreset is independent of the number of points and construction time is near linear in k. The authors use the coreset to get a (1+\epsilon) approximation for the kernel k-me... | SP:e859add2ee645d7c124b0924f9a7d978ee98d8d8 |
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm | 1 INTRODUCTION . The intrinsic characteristics of classical reinforcement learning ( RL ) algorithms , such as temporaldifference ( TD ) learning ( Sutton & Barto , 2018 ) and Q-learning ( Watkins & Dayan , 1992 ) , are based on the expectation of discounted cumulative rewards that an agent observes while interacting w... | Distributional reinforcement learning (DRL) is a family of RL algorithms that estimates distributions of the value function rather than the expectation. The first part of the paper interprets distributional RL as adding a cross-entropy regularizer to the traditional objective. The second part discusses several advantag... | SP:2b070b11752ea3815dc7e4edec978b8de9d9e85e |
DEEP GRAPH TREE NETWORKS | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) , a class of neural networks for learning on graph structured data , have been successfully applied in many areas to solve real world problems , such as link predictions in social networks ( Fan et al. , 2019 ) , pattern recognition ( Ju et al. , 2020 ; Shi & Rajkumar , 2... | The paper studies the task of network embedding. The authors propose a Graph Tree Networks (GTree), a deep graph neural network architecture that originates from the tree representation of the graphs. In particular, the proposed approach conduct message passing upward from the leaf nodes to the root node to update the ... | SP:273f61a567b01deba29b28d377925fc9968ea69b |
Graph Attention Multi-layer Perceptron | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) generalize convolutional neural networks to graph-structured data and have achieved great success in a wide range of tasks , including node classification , link prediction , and recommendation . ( Kipf & Welling , 2016 ; Hamilton et al. , 2017 ; Bo et al. , 2020 ; Cui et... | The paper proposed the Graph Attention Multi-Layer Perceptron (GAMLP) model that combines features extracted from two streams: 1) node-adaptive features that are obtained via "Graph-wise / layer-wise propagation" (Section 2.2), 2) features obtained from label propagation. The author also proposed two ways for performin... | SP:63893c44301c897bc9fc5b539ad2ab3f478b015e |
Intervention Adversarial Auto-Encoder | In this paper we propose a new method to stabilize the training process of the latent variables of adversarial auto-encoders , which we name Intervention Adversarial auto-encoder ( IVAAE ) . The main idea is to introduce a sequence of distributions that bridge the distribution of the learned latent variable and its pri... | This paper presents Intervention Adversarial auto-encoder to stabilize the training process of the latent variables of adversarial auto-encoders. Intervention Adversarial auto-encoder adopts a sequence of distributions to bridge the distribution of the learned latent variable and its prior distribution. The bridge dist... | SP:033d1dbd758aecb992c0cb3ae49cd06335f2d2a4 |
Graph Piece: Efficiently Generating High-Quality Molecular Graphs with Substructures | Molecule generation , which requires generating valid molecules with desired properties , is a fundamental but challenging task . Recent years have witnessed the rapid development of atom-level auto-regressive models , which usually construct graphs following sequential actions of adding atom-level nodes and edges . Ho... | This paper proposes a new molecular graph generation method and empirically shows its effectiveness. The proposed method first decomposes molecular graphs into smaller parts (called graph pieces), followed by training a variational autoencoder so that it can generate the collected graph pieces. The trained model is exp... | SP:7231ca0a1ddceb9d867a4355b1207c6b7ce43e99 |
InstaHide’s Sample Complexity When Mixing Two Private Images | n O ( kpriv ) priv + poly ( npub ) time for any integer kpriv , where npriv and npub denote the number of images used in the private and the public dataset to generate a mixed image sample . Under the current setup for the InstaHide challenge of mixing two private images ( kpriv = 2 ) , this means n 4/3 priv samples ar... | The paper studies the sample complexity of InstaHide challenge in an idealized setting, where it is assumed that each public and private image is generated using a standard Gaussian distribution. Further, similar to previous attacks, the paper assumes that each image is a combination of 2 private images. The main cont... | SP:d8ddd2bef5bca3680e6602681fecf78f4d5c541d |
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Multi-Agent Reinforcement Learning ( Littman , 1994 ; Zhang et al. , 2019 ) generalizes Reinforcement Learning ( Sutton and Barto , 2018 ) to address the sequential decision-making problem of multiple agents maximizing their individual long term rewards while interacting with each other in a common env... | This paper deals with a class of cooperative MARL problems with permutation invariance. It first shows that, for such problems, there exists an optimal policy that is permutation invariant, and the value function can be characterized as a function of the local state of one agent and the empirical state distribution ove... | SP:b86072dda30da18ae4f5395e4a38ebd369f71f66 |
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning | 1 INTRODUCTION . The ability to generalize to data not seen during training is essential for the widespread trust and adoption of machine learning models . In practical applications of machine learning , we typically train and fine-tune on a dataset which are in-distribution ( ID ) with each other , but when deployed t... | This paper highlights important variables impacting the effective robustness (ER) of a pre-trained, fine-tuned model. The authors identify that increasing model size, dataset size, and example difficulty improves the ER of a pre-trained, fine-tuned model. The experiments suggest that the zero-shot component of CLIP pl... | SP:bebf3b45f6f5f483a9ed421a219464eb126894b7 |
FEDERATED LEARNING FRAMEWORK BASED ON TRIMMED MEAN AGGREGATION RULES | 1 INTRODUCTION . As one special case of distributed machine learning , federated learning ( FL ) draws increasing research attention recently . FL has become one promising approach to enable clients collaboratively to learn a shared model with the decentralized and private data on each client node . Thus it is of centr... | This paper considers the trimmed mean function as the aggregation rule for the byzantine resilient distributed learning. The authors provide a theoretical convergence for strongly convex objectives. Besides, the authors empirically compare trimmed mean with krum and average. | SP:3749845c5e03850aef1476d5310628a4a1fd6c8e |
Orthogonalising gradients to speedup neural network optimisation | 1 INTRODUCTION Neural network layers are made up of several identical , but differently parametrised , components , e.g . filters in a convolutional layer , or heads in a multi-headed attention layer . Layers consist of several components so that they can provide a diverse set of intermediary representations to the nex... | This paper proposes to orthogonalize the gradients of each neuron (within a layer) in order to improve training dynamics. They build on the assumption that neurons in a layer tend to learn similar features early in the training, and thus pushing them in orthogonal directions would yield to better results. The method re... | SP:a1ddf78410fb02d91305db84d2423308a600eb0e |
The MultiBERTs: BERT Reproductions for Robustness Analysis | Experiments with pre-trained models such as BERT are often based on a single checkpoint . While the conclusions drawn apply to the artifact tested in the experiment ( i.e. , the particular instance of the model ) , it is not always clear whether they hold for the more general procedure which includes the architecture ,... | Many tasks in contemporary NLP begin by building off of a large language model. This can cast the downstream task as a sort of fine-tuning experiment, whereby the results are heavily influenced by conditioning on the starting point of a single pre-trained version of an LLM. In this work, the authors take BERT as an e... | SP:7c5d3b908b218663706b12dddbfb60a57afb7077 |
Text Generation with Efficient (Soft) $Q$-Learning | 1 INTRODUCTION . Recent natural language generation systems have made remarkable progress in producing wellformed coherent text , especially with the massive pretrained language models ( LMs ) ( Radford et al. , 2019 ; Brown et al. , 2020 ; Lewis et al. , 2020 ; Raffel et al. , 2019 ) . Those models are typically train... | This paper considers the problem of learning text generation models using reinforcement learning. The problem is challenging in that RL algorithm becomes inefficient or unstable when dealing with large action space and the sparse reward situations in text generation. To address these problems, this paper adapts the pat... | SP:541930751a63c710868146722f960e68c1d86212 |
The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning | 1 INTRODUCTION . John is a telecommunication engineer . His day job is to operate a mobile antenna . He has about forty knobs to turn , in principle every five minutes , based on about a hundred external and internal system observables . His goal is to keep some performance indicators within operational limits while op... | The authors study model-based reinforcement learning applied in the scenario of iterated batch reinforcement learning. There exist other namings of the studied in the paper scenario of iterated batch reinforcement learning, I found out that there exist several other synonymous names of such setting: growing batch RL/ m... | SP:5b5f025ee45876731eb9bcb91225b47aa9ea88a3 |
ScaLA: Speeding-Up Fine-tuning of Pre-trained Transformer Networks via Efficient and Scalable Adversarial Perturbation | The size of transformer networks is growing at an unprecedented rate and has increased by three orders of magnitude in recent years , approaching the trillions . To train models of increasing sizes , researchers and practitioners have employed large-batch optimization to leverage massive distributed deep learning syste... | In this paper, the authors propose ScaLA to speed up the fine-tuning of large pre-trained transformer language models. Specifically, ScaLA employs adversarial training to solve the worse convergence in large-batch training. Several techniques are proposed to reduce the computational overhead. Experiments on the GLUE be... | SP:fe59178f5c1e3ee525853ee6a3963f54e3cb34ef |
Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks | 1 INTRODUCTION . Learning in high dimensions is commonly believed to suffer from the curse of dimensionality , in which the number of samples required to solve the problem grows rapidly ( often polynomially ) with the dimensionality of the input . Nevertheless , modern neural networks often exhibit an astonishing power... | The paper categorizes architectural biases from a frequency point of view through the lens of NTKs and NNGPs. The two main quantities are the frequency index (FI) and the spatial index (SI) which are calculated from the DAGs. The authors claim that these two quantities capture what type of functions (in terms of freque... | SP:de75a12ebc83164b716c42e0cad6d65747dcbbd2 |
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction | 1 INTRODUCTION AND RELATED WORK . One of the most fundamental tasks in machine learning is to learn and model complex distributions ( or structures ) of real-world data , such as images or texts , from a set of observed samples . By “ learn and model ” , one typically means that we want to establish a ( parameteric ) m... | The paper "Closed-Loop Data Transcription To An LDR via Minimaxing Rate Reduction" introduces a two-player minimax game between an encoder and a decoder to yield a linear discriminative representation (LDR). It achieves this by building on top of the recently proposed MCR2 rate reduction principle, and then motivates a... | SP:0566be78283de283efd68c0a1062e1fcc6c7bad5 |
Neural Methods for Logical Reasoning over Knowledge Graphs | 1 INTRODUCTION . Knowledge graphs ( KGs ) are a type of data structure that can capture many kinds of relationships between entities ( e.g . : Moscow cityIn−−−→ Russia ) and have been popularized since the creation of the semantic web or its introduction into Google ’ s search engine . They can contain many kinds of di... | The article presents a new system for solving multi-hop queries on knowledge graphs. The system allows queries to be performed by handling operators using one- and two-input MLPs. This formulation also makes it possible to handle negative queries. The paper also presents a second version of the presented system that e... | SP:7a7a78ede395707824bd3ea07930ca75388e9eeb |
Benchmarking Machine Learning Robustness in Covid-19 Spike Sequence Classification | The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 viral genome — millions of sequences and counting . This amount of data , while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity , dynamics and evolu... | This paper presents a framework to test the accuracy and robustness of different machine learning algorithms in classifying the COVID-19 spike sequences. It benchmarks Naive Bayes, Logistic regression, Ridge regression, and fully-connected neural network architectures. It also explores k-mer and one hot encoding repres... | SP:c3e7365d31c567dd3ac59851549b08f505396158 |
Interrogating Paradigms in Self-supervised Graph Representation Learning | 1 INTRODUCTION . Analyzing graph-structured data is essential for many real-world applications and graph neural networks ( GNNs ) have emerged as a popular solution for challenging prediction tasks . However , these tasks often have limited labeled data due to prohibitive procuration costs and require models to possess... | This paper focuses on the augmentation of graph contrastive learning. Authors make further explorations on graph contrastive learning based on the previous CV contrastive work (HaoChen et al. (2021) ) and hope to design a good benchmark by analyzing the relationship between GCL/auto-encoding methods and augmentations. ... | SP:c5226d60fb9d3459cde51541282a43ba3d5c445d |
Differentially Private SGD with Sparse Gradients | 1 INTRODUCTION . The success of machine learning , and deep neural networks in particular , combined with ubiquitous edge computation and digital record keeping , has led to a surge in privacy sensitive learning applications . Internet-scale data promises to accelerate the development of data-driven statistical approac... | This paper considers the problem of differentially privately learning deep neural networks. In order to improve the accuracy and reduce the communication cost, this paper proposes to randomly freeze a progressively increasing subset of parameters, which results in sparse gradient updates. Empirical results also show th... | SP:11163a354be2ff895b7cfe45b3048ce7239f018f |
FedProf: Selective Federated Learning with Representation Profiling | 1 INTRODUCTION . With the advances in Artificial Intelligence ( AI ) , we are seeing a rapid growth in the number of AI-driven applications as well as the volume of data required to train them . However , a large proportion of data used for machine learning are often generated outside the data centers by distributed re... | In this paper, the authors propose a user selection algorithm for federated learning (FL). The key motivation is to select high quality clients for update and thus to reduce the impact of low quality data on FL training. A hidden hypothesis is that high quality data has similar representations while noisy and low-quali... | SP:c840b9aeecbbce27b1f5d1e9773f5eaf249a0806 |
Unified Visual Transformer Compression | 1 INTRODUCTION . Convolution neural networks ( CNNs ) ( LeCun et al. , 1989 ; Krizhevsky et al. , 2012 ; He et al. , 2016 ) have been the de facto architecture choice for computer vision tasks in the past decade . Their training and inference cost significant and ever-increasing computational resources . Recently , dra... | This paper proposed a unified ViT compression framework that assembles pruning, layer skipping, and knowledge distillation as one. By adding the sparsity loss and the resources loss into the objective, ViT models can be stably trained at a high sparse ratio. By introducing the knowledge distillation loss, the compresse... | SP:2d58776ea12096986ca728229d06765779ebad50 |
Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games | 1 INTRODUCTION . Multi-agent reinforcement learning ( MARL ) has been the fundamental driver of numerous recent advances in Artificial Intelligence ( AI ) and Machine Learning ( ML ) ranging from super-human performance in competitive game-playing ( Silver et al. , 2016 ; 2018 ; Brown & Sandholm , 2019 ; Jaderberg et a... | The paper introduces the Markov Potential Game (MPG), which generalizes the classical potential game. The authors then point out several properties of MPG. It proves the convergence to $\epsilon$-NE for learning agents following independent policy gradient or stochastic gradient. The paper also presents the experiment ... | SP:5e61fb0b1e9e65ced941ad735535761280b33058 |
Distributed Zeroth-Order Optimization: Convergence Rates That Match Centralized Counterpart | Zeroth-order optimization has become increasingly important in complex optimization and machine learning when cost functions are impossible to be described in closed analytical forms . The key idea of zeroth-order optimization lies in the ability for a learner to build gradient estimates by queries sent to the cost fun... | This paper proposes a zeroth-order optimization algorithm for distributed, multi-agent systems with time-varying communication networks. The authors show that their presented multi-agent zeroth-order projection averaging algorithm (and its improved multi-stage version) has a convergence rate that matches the centralize... | SP:b249ace6538513759286988a08e2be87b7fe4f04 |
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects | Perceiving and manipulating 3D articulated objects ( e.g. , cabinets , doors ) in human environments is an important yet challenging task for future home-assistant robots . The space of 3D articulated objects is exceptionally rich in their myriad semantic categories , diverse shape geometry , and complicated part funct... | This paper is solving the problem of pushing and pulling objects (mostly things like cabinets) by learning visual action trajectories proposals via a curiosity-driven RL / Perception joint training. The system input point clouds the object and outputs the actionable score and the per-trajectory success likelihood scor... | SP:864954858dee11b824ad1f786685e3c4d33a6d75 |
Deep Reinforcement Learning for Equal Risk Option Pricing and Hedging under Dynamic Expectile Risk Measures | 1 INTRODUCTION . This paper considers solving risk-averse dynamic decision making problems arising from applications where risk needs to be evaluated according to risk measures that are coherent . In particular , we draw our motivation from the financial application of equal-risk pricing ( ERP ) and hedging ( Guo & Zhu... | The paper offers a solution to the problem of Equal Risk Pricing (ERP) by framing it as a risk-sensitive MDP formulation. In particular, the authors considers a dynamic risk-measures using expectiles measures as conditional risk-measure for each step. An extension of DDPG is derived by exploiting the properties of the ... | SP:a5f92cc855c1b6a188be22bf49090cac1b9d241c |
Is Homophily a Necessity for Graph Neural Networks? | 1 INTRODUCTION Graph neural networks ( GNNs ) are a prominent approach for learning representations for graph structured data . Thanks to their great capacity in jointly leveraging attribute and graph structure information , they have been widely adopted to promote improvements for numerous graph-related learning tasks... | The paper revisited the performance of GCN on graph with heterophily and provide negative evidence that heterophily does not always result in the poor performance of GCN, which contradicts with the assumptions/observations of many previous papers. They demonstrated that the GCN embeddings are still label-distinguishabl... | SP:e733b8bfbcc68c8cd2d535378f89800fbce902c7 |
A generalization of the randomized singular value decomposition | 1 INTRODUCTION . Computing the singular value decomposition ( SVD ) is a fundamental linear algebra task in machine learning ( Paterek , 2007 ) , statistics ( Wold et al. , 1987 ) , and signal processing ( Alter et al. , 2000 ; Van Der Veen et al. , 1993 ) . The SVD of an m × n real matrix A with m ≥ n is a factorizati... | This paper proposes a generalization approach for the randomized SVD. In the existing approach of the randomized SVD, a standard Gaussian random matrix is used to reduce the size of a matrix to perform SVD. The proposed approach uses a multivariate Gaussian distribution with a covariance matrix instead of the standard ... | SP:010e3bb657a3e5a7a39576ce2c8e82ad77894a13 |
CheXT: Knowledge-Guided Cross-Attention Transformer for Abnormality Classification and Localization in Chest X-rays | 1 INTRODUCTION . In medical study , handcrafted radiomics ( Zwanenburg et al. , 2016 ) refers to the process of extracting several quantitative and semiquantitative features from medical images for improved decision support . It has the potential to uncover disease characteristics that are difficult to identify by view... | This paper presents a Transformer-based model CheXT for abnormality classification and localization from chest X-rays with auxiliary modality of radiomic features via a feedback loop module. The output tokens from image and radiomics branches are fused by a cross-attention module for the localization and the disease cl... | SP:a1595e4fffaaddf6d7d0708456c9ef19c89f7840 |
Distributionally Robust Models with Parametric Likelihood Ratios | 1 INTRODUCTION . It is well acknowledged that modern neural network based machine learning models tend to underperform when they are evaluated on data distributions that differ from the one they were trained on . For example , machine learning model performance has been observed to degrade under train-test mismatch in ... | As far as I can see, this work is about practical methods for distributionally robust optimization (DRO) with a special focus on trying to overcome the limitations of previous methods that have been proposed for this kind of problem. This work builds on the idea of instance-reweighting of the loss function via a re-wei... | SP:306987e867cca43b12cabb31f2ab1449a1780c73 |
Generalized Fourier Features for Coordinate-Based Learning of Functions on Manifolds | 1 INTRODUCTION . Recent breakthroughs on learning representations of 3D shapes ( Mescheder et al. , 2019 ; Park et al. , 2019 ; Sitzmann et al. , 2019 ) or scenes ( Mildenhall et al. , 2020 ) employ the so-called “ coordinatebased ” networks , which take low-dimensional coordinates as inputs and approximate a continuou... | The paper generalizes the commonly used sinusoidal position encoding scheme (such as that in NerF input encoding) to inputs naturally residing in non-Euclidean manifold. This is achieved by representing the input coordinates as projections on alternative sets of orthornormal bases instead of the trivial Euclidean co... | SP:c6f3f96922127f672a92b6911c1b8e29e210074d |
On the Connection between Local Attention and Dynamic Depth-wise Convolution | 1 INTRODUCTION . Vision Transformer ( Chu et al. , 2021b ; d ’ Ascoli et al. , 2021 ; Dosovitskiy et al. , 2021 ; Guo et al. , 2021 ; Han et al. , 2020 ; Khan et al. , 2021 ; Touvron et al. , 2020 ; Wang et al. , 2021b ; Wu et al. , 2021 ; Xu et al. , 2021 ; Yuan et al. , 2021b ) has shown promising performance in Imag... | Recently local attention based vision transformers achieved state-of-the-art results on various visual recognition tasks. This paper rephrases local attention as a channel-wise spatially-locally connected layer with dynamic connection weights. By analyzing local attention form the view of sparse connectivity, weight sh... | SP:df325139c17effd3c3bfb932cbf44ef230559261 |
Manifold Distance Judge, an Adversarial Samples Defense Strategy Based on Service Orchestration | Deep neural networks ( DNNs ) are playing an increasingly significant role in the modern world . However , they are weak to adversarial examples that are generated by adding specially crafted perturbations . Most defenses against adversarial examples focused on refining the DNN models , which often sacrifice the perfor... | This paper proposes a manifold distance based detection based against adversarial samples. In addition, it also proposes manifold distance judge for the adversarial defense. The focus of the paper is to make low-dimensional manifolds better express high-dimensional data; the experimental results seems that the proposed... | SP:fc3fbc5ba25cbbe3633a32893f018f9e7318c0ee |
On the Convergence of Certified Robust Training with Interval Bound Propagation | 1 INTRODUCTION . It has been shown that deep neural networks are vulnerable against adversarial examples ( Szegedy et al. , 2014 ; Goodfellow et al. , 2015 ; Carlini & Wagner , 2017 ; Kurakin et al. , 2016 ) , where a human imperceptible adversarial perturbation can easily alter the prediction by neural networks . This... | Training deep neural networks in the presence of adversarial perturbations (in the input data) is a very active research topic. There are lots of works defining notions of robustness, proposing solution algorithms, and introducing algorithmic improvements. Several of the recent techniques involve or extend the interval... | SP:348b7ab8ecfe2e7cfce697d8a1f9917880e95f62 |
CONTEXT AUGMENTATION AND FEATURE REFINEMENT NETWORK FOR TINY OBJECT DETECTION | Tiny objects are hard to detect due to their low resolution and small size . The poor detection performance of tiny objects is mainly caused by the limitation of network and the imbalance of training dataset . A new feature pyramid network is proposed to combine context augmentation and feature refinement . The feature... | The paper aims at the tiny object detection and point out the issues are small context feature, semantic feature conflicts, and less tiny objects in training data. To solve the aforementioned problems, authors introduce context augmentation module (CAM), design a feature refinement module, and adopt data-augmentation ... | SP:46824855f69bc3fda308ee0159a03791dafe4d7a |
Certified Adversarial Robustness Under the Bounded Support Set | 1 INTRODUCTION Vulnerability to adversarial samples is a major obstacle that various classifiers obtained by machine learning algorithms , especially deep neural networks ( DNNs ) , need to overcome ( Szegedy et al. , 2013 ; Nguyen et al. , 2015 ) . For instance , in computer vision applications , deliberately adding s... | The authors provide robustness certification results for smoothed classifiers. Specifically, the considered setting follows that of Dvijotham (2020) except the base smoothing distribution is specified to have bounded support, and the relaxation follows from an intersection of Wasserstein and TV balls. Under this settin... | SP:29a0522752ffb52ec7ea614953efdab3f4d58956 |
Learning affective meanings that derives the social behavior using Bidirectional Encoder Representations from Transformers | 1 INTRODUCTION . Consider talking to your mentor for some advice about how to behave with your colleague . Your mentor probably starts asking questions about the culture in the workspace and may continue asking about the identity of the person . These questions could be about institutional constraints such as being the... | This paper proposes a new sentiment representation method by using Affect Control Theory (ACT) and BERT model. Using ACT could capture the social interactions and sentiment changes. The main contribution of this paper is the usage of ACT and the whole process. According to the experimental results, the BERT model shows... | SP:f8f828468e58832938cfac03ceac0894b8919b4a |
An Operator Theoretic View On Pruning Deep Neural Networks | 1 INTRODUCTION . A surprising , but well-replicated , result in the study of deep neural network ( DNN ) optimization is that it is often possible to significantly “ prune ” the number of parameters after training with little effect on performance ( Janowsky , 1989 ; Mozer & Smolensky , 1989a ; b ; LeCun et al. , 1989 ... | The authors studied network pruning from the perspective of dynamical system theory. They show that a new type of pruning method, named Koopman pruning, unifies magnitude pruning and gradient-based pruning to a degree. It also clarifies aspects of magnitude-based pruning before convergence in training. | SP:c0704b8a3266abd3de42e86e02bb7020f90b90f7 |
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy | 1 INTRODUCTION . Automated methods for neural network design , referred to as neural architecture search ( NAS ) , have been used to find architectures that are more efficient and more accurate than the best manually designed architectures ( Zoph et al. , 2018 ; Real et al. , 2019 ; So et al. , 2019 ) . However , it is... | The authors collected most of the existing NAS benchmarks to construct a new benchmark. A unified API is provided to use these existing search spaces and architecture datasets. Based on this the authors re-analyze some NAS algorithms on this new large and comprehensive benchmark and have some interesting observations. | SP:60f9746771f8a8c53f13dbafebf3ad2cdcec2367 |
Target Propagation via Regularized Inversion | 1 INTRODUCTION . Target propagation algorithms can be seen as perturbative learning alternatives to the gradient backpropagation algorithm , where virtual targets are propagated backward instead of gradients ( Le Cun , 1986 ; Le Cun et al. , 1989 ; Rohwer , 1990 ; Mirowski & LeCun , 2009 ; Bengio , 2014 ; Goodfellow et... | The authors study a variant of target propagation in which targets are computed by solving a sequence of minimization problems. Instead of resorting to iterative methods the authors propose to use an analytical solution. The algorithm is investigated as a recurrent neural network learning algorithm in a number of exper... | SP:66e5ebec53c030720e05ce949998e42a9dc655d6 |
A Sampling-Free Approximation of Gaussian Variational Auto-Encoders | 1 INTRODUCTION . Variational Auto-Encoders ( VAE ) allow generating data , mapping data into a latent space , and modifying data by perturbing it in a controlled manner in the latent space [ 1 , 2 ] . This enables them to generate and modify images [ 3 , 4 ] , speech [ 5 ] , and molecular structures [ 6 ] , or detect o... | The paper proposes an approximation that frees the calculation of ELBOs of Gaussian variational autoencoders from sampling. To achieve this, it utilizes Taylor expansion on the decoder networks. The proposed method was evaluated on three typical datasets. The authors also found that the approximation alleviated the iss... | SP:fc4fe8b6a9c90505e18dae21685b0de3a1f2fdc6 |
How Attentive are Graph Attention Networks? | 1 INTRODUCTION . Graph neural networks ( GNNs ; Gori et al. , 2005 ; Scarselli et al. , 2008 ) have seen increasing popularity over the past few years ( Duvenaud et al. , 2015 ; Atwood and Towsley , 2016 ; Bronstein et al. , 2017 ; Monti et al. , 2017 ) . GNNs provide a general and efficient framework to learn from gra... | This paper analyzes the limitation of GAT by pointing out that GAT computes a limited kind of attention: static attention. This paper then introduces a simple fix by modifying the order of operations and proposes GATv2: a dynamic attention variant. Experiments show that GATv2 can outperform GAT. | SP:dfdfd08fcf35668935298ab57e6b6626d277e5dd |
Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data | 1 INTRODUCTION . Existing machine learning methods , particularly deep learning models , typically require big data to pursue remarkable performance . For instance , conditional deep generative models are able to generate high-fidelity and diverse images , but they have to rely on vast amounts of labeled data ( Lucic e... | This paper targets at relieving the massive labeled data consumption of deep learning through the framework of semi-supervised learning. In particular, it finds out that two training approaches, Positive-Unlabeled classification and the conditional generation, can benefit each other. Jointly conducting these two approa... | SP:bf9538a602859eaf9e0c3138c5e46c782863a054 |
Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks | 1 INTRODUCTION . Many impressive successes in machine learning are reached through neural network architectures with a huge number of trainable parameters . Consequently , substantial research in the field aims at reducing the size of such networks while maintaining good accuracy , e.g . for deployment in resource cons... | The authors provide theoretical analysis on the lottery ticket hypothesis for convolutional neural networks. The techniques of the random subset sum are used to prove theorems. The authors start with introducing the case of a single convolutional kernel and then generalize to the case of the whole convolutional neural ... | SP:ba7c73fbdb6515240ef43aaa93a0894af771d1c7 |
STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection | We present a residual-style architecture for interpretable forecasting and anomaly detection in multivariate time series . Our architecture is composed of stacked residual blocks designed to separate components of the signal such as trends , seasonality , and linear dynamics . These are followed by a Temporal Convoluti... | This paper aims to boost the performance of deep neural networks (DNNs) for time series applications by focusing on the characteristics of interpretable forecasting and anomaly detection which are important for real-world time series data. The authors propose an end-to-end trainable DNN architecture which is composed o... | SP:5b01a386e24c24a0ad1760cf7bd7464cc61645ef |
Re-evaluating Word Mover's Distance | 1 INTRODUCTION The optimal transport ( OT ) distance is an effective tool for comparing probabilistic distributions . Applications of OT include image processing ( Ni et al. , 2009 ; Rabin et al. , 2011 ; De Goes et al. , 2012 ) , natural language processing ( NLP ) ( Kusner et al. , 2015 ; Rolet et al. , 2016 ) , biol... | This paper re-evaluates WMD and identifies issues with the original paper. It shows that the gain from the original paper is not the product of WMD but the normalization. When the normalization is controlled, WMD performs similarly to baseline. Finally, it shows WMD resembles classic BOW when normalization is controlle... | SP:43f992caf8e44161143c89d2a947a21393d17f3b |
A multi-domain splitting framework for time-varying graph structure | 1 INTRODUCTION . In the analysis of spatial-temporal structured data , graph signal processing ( GSP ) is an important type of method , taking advantages of graph model to represent the structure . Graph has abundant features , which should be captured by appropriate rules . These methods can be divided into 2 categori... | In this paper, the authors considered the problem of time-varying graph structure anomaly detection. A graph multi-domain splitting framework was proposed by integrating the time, vertex, and frequency features to locate the anomalies. The authors designed a splitting approach for separating the graph sequences into se... | SP:1fe378d812bd4b9722ae0228396d737d1cc76be2 |
$$Research on fusion algorithm of multi-attribute decision making and reinforcement learning based on intuitionistic fuzzy number in wargame environment$$ | 1 INTRODUCTION . Artificial intelligence ( AI ) and machine learning ( ML ) are becoming increasingly popular in realworld applications . For example , AlphaGo has attracted huge attention in the research community and society by showing the capability of AI defeating professional human players in the board game Go . Y... | This paper explores training a deep reinforcement learning agent to play a wargame. To my understanding, a hand-crafted feature extraction, “multi-attribute decision making”, and a hand-crafted reward shaping module were used to create a dataset of pre-training data for the agent. The resulting PPO agent was shown to o... | SP:894582a4fb8f14e4e9e5f88daff0207320cccef2 |
Randomized Signature Layers for Signal Extraction in Time Series Data | 1 INTRODUCTION . Modeling time series is a common task in finance , physics , and engineering . A frequent challenge is finding a transformation mapping a newly observed time series into a target one ( seq2seq modeling ) or into a label summarizing its salient properties ( classification ) . In the absence of any princ... | This paper proposes a Signature-based method for learning feature representation of time series, denoted as $d$-dimensional path $X$. By definition, Signature is a sequence of $M$ tensors where each tensor is a sum of integral terms defined on dimensions of $X$. Theoretically Signature is guaranteed to linearly explai... | SP:b82519f51f99145432f1a9b910dad6db02515a44 |
Recurrent Model-Free RL is a Strong Baseline for Many POMDPs | 1 INTRODUCTION While reinforcement learning ( RL ) is often cast as the problem of learning a single fully observable task , also known as MDP , training and testing on that same task , most real-world applications of RL demand some degree of transfer and partial observability . For example , visual navigation ( Zhu et... | This paper is concerned with understanding when RNNs are useful in POMDPs. They should that, for a class of POMDPS (e.g., meta RL, robust RL), standard RNNs can be competitive with solutions that are tailored to the given POMDP structure. The authors discuss four design considerations that they claim are essential for ... | SP:9147a661714557dc78e84fcdc676258c434c048d |
Neural network architectures for disentangling the multimodal structure of data ensembles | 1 INTRODUCTION . Neural networks are being employed increasingly in high-stakes application areas , such as face recognition [ Taigman et al . ( 2014 ) ; Huang ( 2012 ) ; Sun et al . ( 2013 ) ; Chen et al . ( 2015 ) ; Xiong et al . ( 2016 ) ] , and medical technologies [ Kermany et al . ( 2018 ) ; Madani et al . ( 2018... | This paper is an attempt to connect tensor factor analysis with DNN learning. The paper explores the forward causal inference and inverse causal inference problems. The forward causal inference is performed using causal capsules architecture that uses M-mode SVD for subspace learning (autoencoder). The authors show tha... | SP:935749d9ae4f9c3c109650a3d303285adf15a117 |
Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder | 1 Introduction . Representation learning of biological sequences is important for data exploration and downstream tasks such as protein design ( Detlefsen et al. , 2020 ; Alley et al. , 2019 ) . Deep generative models such as variational autoencoders ( VAEs ) ( Kingma & Welling , 2013 ; 2019 ) have been especially usef... | The authors introduce a VAE for modeling individual protein families that incorporates phylogenetic trees through an OU process on latent space. They also use a sequence likelihood which does not factorize over positions. The authors claim these two advances represent a more expressive and efficient model of protein ev... | SP:ceb5a9157bf76148cd4ad91c986700cc13a746e9 |
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms | 1 INTRODUCTION . Nowadays , deep learning techniques have been frequently used in sentiment analysis ( Tan et al. , 2013 ; Mukhtar & Khan , 2018 ; Liao et al. , 2021 ) , particularly in movie recommendations and product reviews and ratings . In the healthcare domain , sentiment analysis with deep learning provides many... | This paper proposes a schema-based GNN method to measure the severity of depression. To gain a global representation of each word, the proposed method constructs word nodes and uses schema structure to capture the context-level information. The main contribution of this paper is the introduction of the schema encoder. ... | SP:78856cb083aaa3ec5e98047bea34695240c74477 |
Escaping Stochastic Traps with Aleatoric Mapping Agents | When extrinsic rewards are sparse , artificial agents struggle to explore an environment . Curiosity , implemented as an intrinsic reward for prediction errors , can improve exploration but fails when faced with action-dependent noise sources . We present aleatoric mapping agents ( AMAs ) , a neuroscience inspired nove... | This paper suggests an intrinsic bonus for exploration that avoids noisy TV by adding a penalty for the estimated variance of the reached state $S_{t+1}$ given previous state $S_{t}$. For this, they fit an independent normal model of the new state $S_{t+1}$ with mean ($\mu_{t+1}$) and var ($\sigma_{t+1}^2$) predicted ... | SP:caf2cc9159de2ab140c0337fbd87db251f8f890b |
Monotonic Improvement Guarantees under Non-stationarity for Decentralized PPO | 1 INTRODUCTION . In cooperative multi-agent reinforcement learning ( MARL ) , a team of agents must coordinate their behavior to maximize a single cumulative return ( Panait & Luke , 2005 ) . In such a setting , partial observability and/or communication constraints necessitate the learning of decentralized policies th... | This paper provides analysis showing monotonic improvements when in cooperative MARL settings, where independent, as opposed to joint ratios (over agents) are used. From a single agent’s perspective, a non-stationary distribution is experienced since other players are simultaneously performing updates to their policies... | SP:68ecfb4419e38696c2d36f59cf7aae4569a56c8e |
Can Reinforcement Learning Efficiently Find Stackelberg-Nash Equilibria in General-Sum Markov Games? | 1 INTRODUCTION . Reinforcement learning ( RL ) has achieved striking empirical successes in solving complicated realworld sequential decision-making problems ( Mnih et al. , 2015 ; Duan et al. , 2016 ; Silver et al. , 2016 ; 2017 ; 2018 ; Agostinelli et al. , 2019 ; Akkaya et al. , 2019 ) . Motivated by these successes... | The paper studies the problem of multi-agent Markov games with one leader and multiple followers. Moreover, the games considered are controlled by the leader, meaning that the transition function only depends on the action of the leader. The goal is to find the Stackelberg-Nash equilibrium of the game by means of reinf... | SP:30c0a536a6fd499a14ec394c948c0d87eb47a9eb |
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs | 1 INTRODUCTION . When will a generative adversarial network ( GAN ) trained with samples from a distribution D actually output samples from a distribution that is close to D ? This question is one of the most foundational questions in GAN theory—indeed , it was raised since the original paper introducing GANs . However... | This paper studies the problem of learning generative adversarial networks using a ploy-size ReLU generator and discriminator under the standard Wasserstein-1 metric. The main result is that there exists a "bad" generator that can cheat all discriminators under the estimation of the Wasserstein-1 metric while being far... | SP:758657fd14760eaf9183ac60344d08267b6d6c56 |
Robust Losses for Learning Value Functions | 1 INTRODUCTION . Learning value functions from off-policy data remains an open challenge due to high-variance samples and the inability to optimize the objective of interest . Progress towards this goal has been made over years of algorithm development , by reducing the variance for temporal difference ( TD ) algorithm... | This paper starts with the premise that squared error minimization, despite its wide use, might not be the most effective option for learning value functions. The authors hypothesize that this might be because of squared error's emphasis on outlier states where the bellman error is large at the expense of accuracy on o... | SP:960d665d3bb325ea587d49ce1c4b0f2dea0a0bdf |
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