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BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS | 1 INTRODUCTION . Federated learning ( FL ) is an emerging collaborative machine learning trend with many applications such as next word prediction for mobile keyboards ( McMahan & Ramage , 2017 ) , medical imaging ( Sheller et al. , 2018a ) , and intrusion detection for IoT ( Nguyen et al. , 2019 ) . In FL , clients lo... | This paper provides an interesting research direction for the cross-domain of federating learning and backdoor attacks. This direction has very limited work until the recent 2 years. The work being proposed in this manuscript is simple and straightforward to implement. The pipeline has been clearly demonstrated. The ex... | SP:aeb3b57c2e2f7f7dfba24ee77e4aab2f445b947f |
Meta-k: Towards Unsupervised Prediction of Number of Clusters | 1 INTRODUCTION . Clustering is an important task in machine learning , and it has a wide range of applications ( Lung et al . ( 2004 ) ; Aminzadeh & Chatterjee ( 1984 ) ; Gan et al . ( 2007 ) ) . Clustering often consists of two steps : the feature extraction step and the clustering step . There have been numerous work... | The reviewed paper presents a completely unsupervised framework Meta-K for predicting the number of clusters. The approach advocated in the paper comprises two main parts: autoencoder for feature extraction and multilayer perceptron (MLP) for predicting the number of clusters. Autoencoder is used if necessary to decrea... | SP:9fe7211c656c5142368a867229540e5653a5edab |
Meta-k: Towards Unsupervised Prediction of Number of Clusters | 1 INTRODUCTION . Clustering is an important task in machine learning , and it has a wide range of applications ( Lung et al . ( 2004 ) ; Aminzadeh & Chatterjee ( 1984 ) ; Gan et al . ( 2007 ) ) . Clustering often consists of two steps : the feature extraction step and the clustering step . There have been numerous work... | The paper uses policy gradients in a bandit setting to learn the optimal number of clusters, k, in k-means clustering based on the silhouette score. Finding k that leads to the highest silhouette score is a more specific problem that what the paper title promises. The approach is well-described and supported by experi... | SP:9fe7211c656c5142368a867229540e5653a5edab |
Self-supervised Representation Learning with Relative Predictive Coding | 1 INTRODUCTION . Unsupervised learning has drawn tremendous attention recently because it can extract rich representations without label supervision . Self-supervised learning , a subset of unsupervised learning , learns representations by allowing the data to provide supervision ( Devlin et al. , 2018 ) . Among its ma... | This paper presents a new contrastive representation objective that has good training stability, minibatch size sensitivity, and downstream task performance. This objective is a generalization of Chi-square divergence, the optimal solution is the density ratio of joint distribution and product of marginal distributions... | SP:0783e842aa0246f8c1726f19d3f36e3abe6b3654 |
Self-supervised Representation Learning with Relative Predictive Coding | 1 INTRODUCTION . Unsupervised learning has drawn tremendous attention recently because it can extract rich representations without label supervision . Self-supervised learning , a subset of unsupervised learning , learns representations by allowing the data to provide supervision ( Devlin et al. , 2018 ) . Among its ma... | This paper proposes a new objective for self-supervised contrastive learning. In the general framework proposed by Tsai et al. (2020b), the proposed method boils down to using a divergence related to $\chi^2$-divergence. Compared to other objectives for contrastive learning, the authors illustrate the advantages of the... | SP:0783e842aa0246f8c1726f19d3f36e3abe6b3654 |
ABS: Automatic Bit Sharing for Model Compression | 1 INTRODUCTION . Deep neural networks ( DNNs ) have achieved great success in many challenging computer vision tasks , including image classification ( Krizhevsky et al. , 2012 ; He et al. , 2016 ) and object detection ( Lin et al. , 2017a ; b ) . However , a deep model usually has a large number of parameters and cons... | Inspired by gradient-based NAS of single-path formulation, the authors propose a super-bit model, a single-path method, to decide the optimal number of quantization bits and pruning of a group of filters. While it can be a time-consuming process to study the impact of quantization of certain filters (or layers) on mode... | SP:3e812bc034c95a7141296dd879217ce10d01065a |
ABS: Automatic Bit Sharing for Model Compression | 1 INTRODUCTION . Deep neural networks ( DNNs ) have achieved great success in many challenging computer vision tasks , including image classification ( Krizhevsky et al. , 2012 ; He et al. , 2016 ) and object detection ( Lin et al. , 2017a ; b ) . However , a deep model usually has a large number of parameters and cons... | The paper describes the method to determine optimal quantization bit-width and pruning configuration for the neural network compression. Different from other approaches, the proposed method integrates multiple bit configurations (including pruning) into a single architecture, which is named “Super-bit”. The architectur... | SP:3e812bc034c95a7141296dd879217ce10d01065a |
Teaching Temporal Logics to Neural Networks | 1 INTRODUCTION . Machine learning has revolutionized several areas of computer science , such as image recognition ( He et al. , 2015 ) , face recognition ( Taigman et al. , 2014 ) , translation ( Wu et al. , 2016 ) , and board games ( Moravcı́k et al. , 2017 ; Silver et al. , 2017 ) . For complex tasks that involve sy... | The paper explores the application of modern learning techniques (transformers and tree positional encodings) to the task of producing valid traces for a given LTL specification. A series of experiments are conducted to explore the generalization power of the proposed approach, both in terms of formula size and style o... | SP:1dade549a14dc9c41f2d16be1e405be113c611fd |
Teaching Temporal Logics to Neural Networks | 1 INTRODUCTION . Machine learning has revolutionized several areas of computer science , such as image recognition ( He et al. , 2015 ) , face recognition ( Taigman et al. , 2014 ) , translation ( Wu et al. , 2016 ) , and board games ( Moravcı́k et al. , 2017 ; Silver et al. , 2017 ) . For complex tasks that involve sy... | The paper presents multiple dataset generation and testing procedures for linear temporal logic and propositional logic satisfiability. They are then used to train Transformers with tree positional encoding. The approach amounts to imitation learning based on existing solvers for satisfiability for the considered logic... | SP:1dade549a14dc9c41f2d16be1e405be113c611fd |
Variational Deterministic Uncertainty Quantification | 1 INTRODUCTION . Deploying machine learning algorithms as part of automated decision making systems , such as self driving cars and medical diagnostics , requires implementing fail-safes . Whenever the model is presented with a novel or ambiguous situation , it would not be wise to simply trust its prediction . Instead... | This paper proposes variational deterministic uncertainty quantification (vDUQ), which adopts the stochastic (sparse) variational deep kernel learning (DKL) method to enable uncertainty estimations for deep models. To avoid uncertainty collapse, the deep neural network in the GP kernel is regularized with spectral norm... | SP:0fa6e1dfa434bfef4c0071572e60dbafa0d65d4e |
Variational Deterministic Uncertainty Quantification | 1 INTRODUCTION . Deploying machine learning algorithms as part of automated decision making systems , such as self driving cars and medical diagnostics , requires implementing fail-safes . Whenever the model is presented with a novel or ambiguous situation , it would not be wise to simply trust its prediction . Instead... | 1. In the introduction, the author separately pointed out the issues of DUQ and DKL. However, these issues are not convincing as no citations or theoretical proof is provided in this paper. The notations in the intro are also not well-defined. X, x, x* are used without difference, which however should be clearly define... | SP:0fa6e1dfa434bfef4c0071572e60dbafa0d65d4e |
Loss Landscape Matters: Training Certifiably Robust Models with Favorable Loss Landscape | 1 INTRODUCTION . Despite the success of deep learning in many applications , the existence of adversarial example , an imperceptibly modified input that is designed to fool the neural network ( Szegedy et al. , 2013 ; Biggio et al. , 2013 ) , hinders the application of deep learning to safety-critical domains . There h... | This paper studies why training with looser bounds (IBP) can outperform tighter linear relaxation based methods in certified defense. The authors argue that this is because IBP has a smoother loss landscape compared to linear relaxation based methods. Then the paper proposes to optimize the lower bound in the CROWN rel... | SP:012aea2b0a756ae1714eb20ac4fdb723a644ee8f |
Loss Landscape Matters: Training Certifiably Robust Models with Favorable Loss Landscape | 1 INTRODUCTION . Despite the success of deep learning in many applications , the existence of adversarial example , an imperceptibly modified input that is designed to fool the neural network ( Szegedy et al. , 2013 ; Biggio et al. , 2013 ) , hinders the application of deep learning to safety-critical domains . There h... | In this paper, the authors studied the role of loss landscape in training certifiable robust models. The authors reviewed linear relaxation based methods, and showed that Interval Bound Propagation (IBP) is a special case of linear relaxation based methods. Although linear relaxation based methods have a tighter bound ... | SP:012aea2b0a756ae1714eb20ac4fdb723a644ee8f |
High-Likelihood Area Matters --- Rewarding Correct,Rare Predictions Under Imbalanced Distributions | Learning from natural datasets poses significant challenges for traditional classification methods based on the cross-entropy objective due to imbalanced class distributions . It is intuitive to assume that the examples from rare classes are harder to learn so that the classifier is uncertain of the prediction , which ... | - This paper made a finding that weighting up correct predictions for rare class examples also can help to improve the performance of imbalanced classification. In light of this finding, it proposes the Eureka Loss to add additional gradients for examples belong to rare classes in the high-likelihood area when correct... | SP:a89a7421e4d3b82156edcc03ff8b24fb4df8df41 |
High-Likelihood Area Matters --- Rewarding Correct,Rare Predictions Under Imbalanced Distributions | Learning from natural datasets poses significant challenges for traditional classification methods based on the cross-entropy objective due to imbalanced class distributions . It is intuitive to assume that the examples from rare classes are harder to learn so that the classifier is uncertain of the prediction , which ... | This paper deals with learning imbalanced class distributions. First, it empirically finds that the high-likelihood area for the rare classes benefits classification. Then, based on the findings, it proposes a new learning objective called Eureka Loss, which can be viewed as a combination of the frequency-based and li... | SP:a89a7421e4d3b82156edcc03ff8b24fb4df8df41 |
FLAG: Adversarial Data Augmentation for Graph Neural Networks | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have emerged as powerful architectures for learning and analyzing graph representations . The Graph Convolutional Network ( GCN ) ( Kipf & Welling , 2016 ) and its variants have been applied to a wide range of tasks , including visual recognition ( Zhao et al. , 2019 ; Sh... | This paper proposes FLAG (Free Large-scale Adversarial Augmentation on Graphs), an adversarial data augmentation technique that can be applied to different GNN models in order to improve their generalization. The proposed technique consists on adding adversarial perturbations to the nodes’ features solving the standard... | SP:d872c4d4c7d2495156ce9a1c30dd2696ce1173df |
FLAG: Adversarial Data Augmentation for Graph Neural Networks | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have emerged as powerful architectures for learning and analyzing graph representations . The Graph Convolutional Network ( GCN ) ( Kipf & Welling , 2016 ) and its variants have been applied to a wide range of tasks , including visual recognition ( Zhao et al. , 2019 ; Sh... | This paper investigates adversarial feature augmentation for improving the generalizability of graph neural networks. The authors adopt an existing augmentation algorithm and apply on the nodes of each training graph, and use the perturbed graphs for training. The focus of the paper is extensive experimentation in vari... | SP:d872c4d4c7d2495156ce9a1c30dd2696ce1173df |
Class-Weighted Evaluation Metrics for Imbalanced Data Classification | 1 INTRODUCTION . For a broad range of machine learning ( ML ) tasks , predictive modeling in the presence of imbalanced datasets – those with severe distribution skews – has been a long-standing problem ( He & Garcia , 2009 ; Sun et al. , 2009 ; He & Ma , 2013 ; Branco et al. , 2016 ; Hilario et al. , 2018 ; Johnson & ... | The paper presents a simple addition to the Balanced Accuracy approach - which the authors refer to as ‘importance’. However, there is nothing in the formulation of this concept which requires that this is an importance and could in fact be any form of weighting. The paper evaluates the new metric - but only agains the... | SP:98450ef54b363e5e68b19b7bc1327490d69825fa |
Class-Weighted Evaluation Metrics for Imbalanced Data Classification | 1 INTRODUCTION . For a broad range of machine learning ( ML ) tasks , predictive modeling in the presence of imbalanced datasets – those with severe distribution skews – has been a long-standing problem ( He & Garcia , 2009 ; Sun et al. , 2009 ; He & Ma , 2013 ; Branco et al. , 2016 ; Hilario et al. , 2018 ; Johnson & ... | This paper presents a weighted balanced accuracy to evaulate the performance of multi-class classification. Basically, the performance for a multi-class problem can be evaluated by decomposing the original multi-class problem into a number of binary ones based on one-against-rest manner, and then evaulating the perform... | SP:98450ef54b363e5e68b19b7bc1327490d69825fa |
On the Transfer of Disentangled Representations in Realistic Settings | Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning . While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification , their scalability and... | The paper presents a new, more complex, dataset for the use of disentangled representation learning. The dataset is based on real and simulated images of the trifinger robot platform. There are 7 factors of variation with high-resolution measurements of these factors. The dataset contains over 1 million simulated image... | SP:9da1f7bd8d52bdd28891ec3e0c7eef7d367a5eea |
On the Transfer of Disentangled Representations in Realistic Settings | Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning . While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification , their scalability and... | The authors proposed a unique learning scheme for representation disentanglement. However, unlike infoGAN or ACGAN which explicitly learn disjoint feature representations for describing the attributes of interest (via unsupervised and supervised settings, respectively), the authors chose to address this task in a quest... | SP:9da1f7bd8d52bdd28891ec3e0c7eef7d367a5eea |
Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization | 1 INTRODUCTION . Within the research community , the concentration of efforts towards solving the problem of hyperparameter optimization ( HPO ) has been mainly through sequential model-based optimization ( SMBO ) , i.e . iteratively fitting a probabilistic response model , typically a Gaussian process ( Rasmussen ( 20... | The authors propose a new zero-shot hyper-parameter optimization method based-on the meta-learning framework. The proposed method incorporates two ideas from the meta-learning framework namely the task similarity based on the meta-features and the dataset identification. The former idea is used to achieve the requireme... | SP:201c4028ac02743edfeb90aca191850f67d61445 |
Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization | 1 INTRODUCTION . Within the research community , the concentration of efforts towards solving the problem of hyperparameter optimization ( HPO ) has been mainly through sequential model-based optimization ( SMBO ) , i.e . iteratively fitting a probabilistic response model , typically a Gaussian process ( Rasmussen ( 20... | In this papar, the authors formulated a new objective function for HBO, which included an additional regularization term based on the dataset similarity. The authors used the distance between the meta-features of selected datasets to measure this dataset similarity and assumpted that similar datasets should have simila... | SP:201c4028ac02743edfeb90aca191850f67d61445 |
Learning Representation in Colour Conversion | 1 INTRODUCTION . Colour is an inseparable component of our conscious visual perception and its objective utility spans over a large set of tasks such as object recognition and scene segmentation ( Chirimuuta et al. , 2015 ; Gegenfurtner & Rieger , 2000 ; Wichmann et al. , 2002 ) . Consequently , colour is an ubiquitous... | The motivation for this paper is quite hard to understand. A VQ-VAE is directly applied to convert an image from one colour space to another one. However, the colour space transform is human-defined, usually involving linear and a few non-linear (like selecting the maximum value is HSV) procedures. In this case, the la... | SP:9dbd1488470372dae1baf3d391124e2abac8ea53 |
Learning Representation in Colour Conversion | 1 INTRODUCTION . Colour is an inseparable component of our conscious visual perception and its objective utility spans over a large set of tasks such as object recognition and scene segmentation ( Chirimuuta et al. , 2015 ; Gegenfurtner & Rieger , 2000 ; Wichmann et al. , 2002 ) . Consequently , colour is an ubiquitous... | This paper proposes to study an interesting problem of how color informaiton is structured in the variational autoencoders (VAEs). Several instances of VAEs are trained in an unsupervised manner to perform color space conversion. Both low-level and high-level evaluations are performed to study the local statistics and ... | SP:9dbd1488470372dae1baf3d391124e2abac8ea53 |
An empirical study of a pruning mechanism | 1 INTRODUCTION . Deep learning is currently one of the most powerful machine learning methods . It requires neural network to train , which usually takes a few to hundreds times more weights than training data ( He et al. , 2016 ; Zagoruyko & Komodakis , 2016 ; Huang et al. , 2017 ; Karen & Andrew , 2015 ) . Usually , ... | The paper proposes to answer the question why "a network with the same number of weights as that of the pruned network cannot achieve similar performance when trained from scratch". Then it proposes an hypothesis that the small model "does not utilize all of its weights either". To prove this hypothesis, it goes on to ... | SP:e1ced25c8b1fc9745e6f43c1be529e418d9325f9 |
An empirical study of a pruning mechanism | 1 INTRODUCTION . Deep learning is currently one of the most powerful machine learning methods . It requires neural network to train , which usually takes a few to hundreds times more weights than training data ( He et al. , 2016 ; Zagoruyko & Komodakis , 2016 ; Huang et al. , 2017 ; Karen & Andrew , 2015 ) . Usually , ... | This paper dives into why small pruned networks don’t train as well as large networks. They come up with a measure of weight utilization and claim that networks of all sizes only use a portion of their weights during training, and the imbalance increases during optimization. Additionally, they visualize the accuracy su... | SP:e1ced25c8b1fc9745e6f43c1be529e418d9325f9 |
MixKD: Towards Efficient Distillation of Large-scale Language Models | 1 INTRODUCTION . Recent language models ( LM ) pre-trained on large-scale unlabeled text corpora in a self-supervised manner have significantly advanced the state of the art across a wide variety of natural language processing ( NLP ) tasks ( Devlin et al. , 2018 ; Liu et al. , 2019c ; Yang et al. , 2019 ; Joshi et al.... | This paper applies mixup (Zhang et al., 2018) to augment training data to improve knowledge distillation in NLP tasks . Mixup was originally proposed to augment data for continuous data. To apply mixup to textual data, this paper applies mixup to the word/token embeddings instead of the tokens themselves. Some theoreti... | SP:a1269282da0327aa083fa21ef352a5451667f925 |
MixKD: Towards Efficient Distillation of Large-scale Language Models | 1 INTRODUCTION . Recent language models ( LM ) pre-trained on large-scale unlabeled text corpora in a self-supervised manner have significantly advanced the state of the art across a wide variety of natural language processing ( NLP ) tasks ( Devlin et al. , 2018 ; Liu et al. , 2019c ; Yang et al. , 2019 ; Joshi et al.... | The paper proposes combining the MixUp data augmentation method with teacher-student distillation to improve the fine-tuned performance of BERT on benchmark NLP tasks (GLUE). The problem is important, well-motivated and of interest to a broad base of NLP researchers and practitioners. The paper is clear, and generally ... | SP:a1269282da0327aa083fa21ef352a5451667f925 |
Temporal Difference Uncertainties as a Signal for Exploration | An effective approach to exploration in reinforcement learning is to rely on an agent ’ s uncertainty over the optimal policy , which can yield near-optimal exploration strategies in tabular settings . However , in non-tabular settings that involve function approximators , obtaining accurate uncertainty estimates is al... | This paper proposes to use an intrinsic reward based on uncertainties calculated from temporal difference errors. The approach, called Temporal Difference Uncertainties (TDU), estimates the variance of td errors across multiple (bootstrapped) parameters, for a given state, action, next state and reward, where variabili... | SP:5a114af6b868ac0f8923205ea5257590967110c0 |
Temporal Difference Uncertainties as a Signal for Exploration | An effective approach to exploration in reinforcement learning is to rely on an agent ’ s uncertainty over the optimal policy , which can yield near-optimal exploration strategies in tabular settings . However , in non-tabular settings that involve function approximators , obtaining accurate uncertainty estimates is al... | The authors introduce the use of value function variance (conditioned on state transition) as auxiliary reward promoting exploration during training. The variance is estimated using the bootstrap DQN approach. The main difference with similar methods is that the value uncertainty is not used in a Thompson sampling sche... | SP:5a114af6b868ac0f8923205ea5257590967110c0 |
IOT: Instance-wise Layer Reordering for Transformer Structures | 1 INTRODUCTION . Transformer ( Vaswani et al. , 2017 ) has been the dominant architecture in deep learning models ( Hassan et al. , 2018 ; Ng et al. , 2019 ; Carion et al. , 2020 ; Radford et al. , 2019 ; Dai et al. , 2019 ; Lee et al. , 2019 ; Devlin et al. , 2018 ; Yang et al. , 2019 ; Cai & Lam , 2019 ) . A Transfor... | This work explores instance-wise layer re-ordering in transformers. The key idea is to incorporate classifiers that predict the ordering of sub-layers (self-attention, cross-attention, feed-forward) from the averaged input sequence representation, one classifier each for the encoder and the decoder. During training the... | SP:73630ddbe2f83647f099f921abb79b2c0f937aa9 |
IOT: Instance-wise Layer Reordering for Transformer Structures | 1 INTRODUCTION . Transformer ( Vaswani et al. , 2017 ) has been the dominant architecture in deep learning models ( Hassan et al. , 2018 ; Ng et al. , 2019 ; Carion et al. , 2020 ; Radford et al. , 2019 ; Dai et al. , 2019 ; Lee et al. , 2019 ; Devlin et al. , 2018 ; Yang et al. , 2019 ; Cai & Lam , 2019 ) . A Transfor... | This paper studies the influence of the arrangement order for the internal structure in a single-layer Transformer (they named it as layer order) on the performance. It makes a hypothesis that different layer order has an impact on the performance of the model, and the hypothesis is verified by experiments. Based on th... | SP:73630ddbe2f83647f099f921abb79b2c0f937aa9 |
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs | A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks ( GCNs ) . Such GCNs utilize isotropic kernels and are therefore insensitive to the relative orientation of vertices and thus to the geometry of the mesh as a whole . We propose Gauge Equivariant Me... | Although a mesh embedded in 3D space may be treated as a graph, a graph convolution network uses the same weights for each neighbor and is thus permutation invariant, which is the incorrect inductive bias for a mesh: the neighbors of a node are spatially related and may not be arbitrarily permuted. CNNs, GCNs, and G-C... | SP:fa852f6d762a09e601ec0d78694c23155548b214 |
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs | A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks ( GCNs ) . Such GCNs utilize isotropic kernels and are therefore insensitive to the relative orientation of vertices and thus to the geometry of the mesh as a whole . We propose Gauge Equivariant Me... | The work presents a novel message passing GNN operator for meshes that is equivariant under gauge transformations. It achieves that by parallel transporting features along edges and spanning a space of gauge equivariant kernels. Further, a DFT-based non-linearity is proposed, which preserves the equivariance in the lim... | SP:fa852f6d762a09e601ec0d78694c23155548b214 |
Voting-based Approaches For Differentially Private Federated Learning | 1 INTRODUCTION . With increasing ethical and legal concerns on leveraging private data , federated learning ( McMahan et al. , 2017 ) ( FL ) has emerged as a paradigm that allows agents to collaboratively train a centralized model without sharing local data . In this work , we consider two typical settings of federated... | The paper proposes two approaches (i.e., PATE-FL and Private-kNN-FL) to train a differentially private global model in a federated setting based on [1] and [2]. In PATE-FL, each client first trains a teacher model using their local dataset. The teacher models are used to make noisy predictions on a public dataset. Then... | SP:996e66b927181eb325cadb345bb51e96b8d46923 |
Voting-based Approaches For Differentially Private Federated Learning | 1 INTRODUCTION . With increasing ethical and legal concerns on leveraging private data , federated learning ( McMahan et al. , 2017 ) ( FL ) has emerged as a paradigm that allows agents to collaboratively train a centralized model without sharing local data . In this work , we consider two typical settings of federated... | Federated learning enables distributed clients to train a model without sharing the data with each other. This is typically achieved by a gradient descent type algorithm such as federated averaging. The paper argues that federated learning via gradient updates has issues and proposes to use a voting based method for tr... | SP:996e66b927181eb325cadb345bb51e96b8d46923 |
Neuro-algorithmic Policies for Discrete Planning | 1 INTRODUCTION . One of the central topics in machine learning research is learning control policies for autonomous agents . Many different problem settings exist within this area . On one end of the spectrum are imitation learning approaches , where prior expert data is available and the problem becomes a supervised l... | This work proposes a novel neuro-algorithmic policy architecture for solving discrete planning tasks. It takes a high-dimensional image input and processes it through modified ResNet encoders to obtain a graph cost map and a start/goal heatmap. This is fed into a differentiable Dijkstra algorithm to obtain the shortest... | SP:2003bbcbbf2f16f6e54353a8b6f58c613343ecc0 |
Neuro-algorithmic Policies for Discrete Planning | 1 INTRODUCTION . One of the central topics in machine learning research is learning control policies for autonomous agents . Many different problem settings exist within this area . On one end of the spectrum are imitation learning approaches , where prior expert data is available and the problem becomes a supervised l... | This paper presents a method to train a neural network to predict the time-dependent costs, and start and goal states needed to run time-dependent shortest-path planning in a dynamic 2-D environment. The non-differentiability of the path planning is handled by recent work on differentiating through blackbox combinatori... | SP:2003bbcbbf2f16f6e54353a8b6f58c613343ecc0 |
Contrastive Learning of Medical Visual Representations from Paired Images and Text | 1 INTRODUCTION Medical image understanding has the potential to transform healthcare and has seen rapid progress with the use of deep neural architectures ( Gulshan et al. , 2016 ; Esteva et al. , 2017 ; De Fauw et al. , 2018 ; Rajpurkar et al. , 2018b ) . Yet , with expert-level performance achieved only in some speci... | 1. This paper tackles the medical image understanding problem. The aim of this paper is to learn a generic feature representation for medical image that could benefits downstream tasks like medical image classification, zero-shot classification. The main contribution of this paper is proposing a contrastive loss that t... | SP:92d7b00137258b40bcaf13fd19e032cf4c40b3d8 |
Contrastive Learning of Medical Visual Representations from Paired Images and Text | 1 INTRODUCTION Medical image understanding has the potential to transform healthcare and has seen rapid progress with the use of deep neural architectures ( Gulshan et al. , 2016 ; Esteva et al. , 2017 ; De Fauw et al. , 2018 ; Rajpurkar et al. , 2018b ) . Yet , with expert-level performance achieved only in some speci... | In this work, the authors propose a new model, named ConVIRT to learn the medical visual representation from paired image and textual data in an unsupervised strategy. In ConVIRT, they mainly use a contrastive loss with two modalities (images and texts) as inputs to learn the representation. The experimental results sh... | SP:92d7b00137258b40bcaf13fd19e032cf4c40b3d8 |
Asynchronous Modeling: A Dual-phase Perspective for Long-Tailed Recognition | 1 INTRODUCTION . Past years have witnessed huge progress in visual recognition with the successful application of deep convolutional neural networks ( CNNs ) on large-scale datasets , e.g. , ImageNet ILSVRC 2012 ( Russakovsky et al. , 2015 ) , Places ( Zhou et al. , 2017 ) . Such datasets are usually artificially colle... | This paper works on long-tailed classification. The authors conducted an analysis and claimed that the difference of gradients computed on the head and tail classes plays an important role in the performance drop. The authors then proposed a two-stage approach to first train on the head classes and then train on the ta... | SP:4514e92c7a02cd2765a9cc4b35392594b022fa3e |
Asynchronous Modeling: A Dual-phase Perspective for Long-Tailed Recognition | 1 INTRODUCTION . Past years have witnessed huge progress in visual recognition with the successful application of deep convolutional neural networks ( CNNs ) on large-scale datasets , e.g. , ImageNet ILSVRC 2012 ( Russakovsky et al. , 2015 ) , Places ( Zhou et al. , 2017 ) . Such datasets are usually artificially colle... | This paper proposes an interesting view to analyze the long-tailed problem. It states that the gradients are dominated by the head classes so that the tail classes perform poorly. From this observation, the authors propose a dual-phase approach that first train $W_r, W_c^1$ with only head-class data, and extend to trai... | SP:4514e92c7a02cd2765a9cc4b35392594b022fa3e |
Learning Flexible Classifiers with Shot-CONditional Episodic (SCONE) Training | 1 INTRODUCTION . Few-shot classification is the problem of learning a classifier using only a few examples . Specifically , the aim is to utilize a training dataset towards obtaining a flexible model that has the ability to ‘ quickly ’ learn about new classes from few examples . Success is evaluated on a number of test... | The paper proposes a solution to few-shot meta learning approaches overfitting to the number of shots they are finetuned on, and not generalizing as well as expected to novel shots. In order to mitigate this problem, the paper suggests a parameterization of the meta learner which also conditions on the number of shots ... | SP:447a69bbd183f33b2950448c3d2bd50b7400410e |
Learning Flexible Classifiers with Shot-CONditional Episodic (SCONE) Training | 1 INTRODUCTION . Few-shot classification is the problem of learning a classifier using only a few examples . Specifically , the aim is to utilize a training dataset towards obtaining a flexible model that has the ability to ‘ quickly ’ learn about new classes from few examples . Success is evaluated on a number of test... | This paper proposed an implementation method of using different numbers of shots of data for few-shot learning such as to mitigate the negative effect of "different shots". It optimized the FiLM parameters using meta gradient descent during episodic meta-training with different-shot learning tasks. It conducted the exp... | SP:447a69bbd183f33b2950448c3d2bd50b7400410e |
Constructing Multiple High-Quality Deep Neural Networks: A TRUST-TECH Based Approach | 1 INTRODUCTION . Due to the high redundancy on parameters of deep neural networks ( DNN ) , the number of local optima is huge and can grow exponentially with the dimensionality of the parameter space ( Auer et al . ( 1996 ) ; Choromanska et al . ( 2015 ) ; Dauphin et al . ( 2014b ) ) . It still remains a challenging t... | The paper describes a technique based on the modified generalized gradient descent for finding multiple high-quality local optima of deep neural networks. The search method does not require re-initialization of the model parameters and can be carried out in a single training session. Identified local optima are then us... | SP:0a87278c0da53a0b1989fad7932566b8ddd8634b |
Constructing Multiple High-Quality Deep Neural Networks: A TRUST-TECH Based Approach | 1 INTRODUCTION . Due to the high redundancy on parameters of deep neural networks ( DNN ) , the number of local optima is huge and can grow exponentially with the dimensionality of the parameter space ( Auer et al . ( 1996 ) ; Choromanska et al . ( 2015 ) ; Dauphin et al . ( 2014b ) ) . It still remains a challenging t... | This paper proposes a new method for applying the TRUST-TECH method to the ensemble of deep neural networks (DNNs). When applying TRUST-TECH to a deep neural network, it is difficult to determine the direction and exit point. This paper introduces Dynamic Searching Paths (DSP) to solve these problems. The proposed meth... | SP:0a87278c0da53a0b1989fad7932566b8ddd8634b |
Learning disentangled representations with the Wasserstein Autoencoder | 1 INTRODUCTION . Learning representations of data is at the heart of deep learning ; the ability to interpret those representations empowers practitioners to improve the performance and robustness of their models ( Bengio et al. , 2013 ; van Steenkiste et al. , 2019 ) . In the case where the data is underpinned by inde... | This paper addresses disentanglement in the latent space of autoencoders. To this end, it combines ideas from four existing papers, namely the reconstruction loss of the Wasserstein autoencoder, the regularization term decomposition from the total correlation autoencoder, and entropy estimation using minibatch-weighted... | SP:8b35f7c054e1ac74e0ad260add9723766df8613d |
Learning disentangled representations with the Wasserstein Autoencoder | 1 INTRODUCTION . Learning representations of data is at the heart of deep learning ; the ability to interpret those representations empowers practitioners to improve the performance and robustness of their models ( Bengio et al. , 2013 ; van Steenkiste et al. , 2019 ) . In the case where the data is underpinned by inde... | This paper extends the Wasserstein Autoencoder (WAE) work by splitting the divergence on the variational marginal into 2 terms, akin to what was done in TC-VAE. This enables directly controlling the explicit contribution of the total correlation term, which is likely to contribute to disentanglement more directly. They... | SP:8b35f7c054e1ac74e0ad260add9723766df8613d |
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming | 1 INTRODUCTION . The pursuit of advanced Deep Neural Networks ( DNNs ) causes researchers to construct deeper and wider networks , making them expensive to use in terms of power and time . This increases the need for efficient implementations of these networks . Efficient networks reduce cloud-vendor costs and make it ... | The paper studies the problem of Post-Training Quantization of NNs, where no fine-tuning is performed to quantize the model. In particular, the authors focus on sub-8 bit quantization and propose a novel integer linear programming formulation to find the optimal bit width for a given model size. Additional approaches a... | SP:d9a70ada6ed2324c5b430e0a7a6785b1eb49d3ef |
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming | 1 INTRODUCTION . The pursuit of advanced Deep Neural Networks ( DNNs ) causes researchers to construct deeper and wider networks , making them expensive to use in terms of power and time . This increases the need for efficient implementations of these networks . Efficient networks reduce cloud-vendor costs and make it ... | This paper proposed a set of methods for post-training quantization of dnns. The methods include AdaQuant (which jointly optimizes quantization steps for weight and activation per output activation of each layer), Integer Programming (which determines bit-precision for all the layers), and the batchnorm tuning. The aut... | SP:d9a70ada6ed2324c5b430e0a7a6785b1eb49d3ef |
PGPS : Coupling Policy Gradient with Population-based Search | 1 INTRODUCTION In Reinforcement Learning ( RL ) , an agent interacts with the environment , and its goal is to find the policy that maximizes the objective function , which is generally defined as a cumulative discounted reward . Recently , many researchers have worked on combining deep neural networks and a gradient-b... | Recently, several researchers have been trying to combine the goodnesses of direct policy search approaches (mostly based on evolutionary computation approaches) and those of policy gradient approaches in control tasks. This paper proposes a novel combination of an evolutionary direct policy search and an actor-critic... | SP:c4662d0c24d1744837443315de5f92042cada40b |
PGPS : Coupling Policy Gradient with Population-based Search | 1 INTRODUCTION In Reinforcement Learning ( RL ) , an agent interacts with the environment , and its goal is to find the policy that maximizes the objective function , which is generally defined as a cumulative discounted reward . Recently , many researchers have worked on combining deep neural networks and a gradient-b... | The paper proposes a new method combining evolutionary methods and RL. In particular, the authors combine CEM and TD3 in PGPS. PGPS maintains a population of policies, which interact with the environment to collect data filling the replay buffer. The data in replay buffer is then used to train TD3. PGPS enables informa... | SP:c4662d0c24d1744837443315de5f92042cada40b |
Differentially Private Learning Needs Better Features (or Much More Data) | 1 INTRODUCTION . Machine learning ( ML ) models have been successfully applied to the analysis of sensitive user data such as medical images ( Lundervold & Lundervold , 2019 ) , text messages ( Chen et al. , 2019 ) or social media posts ( Wu et al. , 2016 ) . Training these ML models under the framework of differential... | The paper presents an analysis of differential privacy in machine learning, with a focus on neural networks trained via differentially private stochastic gradient descent (DPSGD). The main focus and the message in the paper is that the handcrafted features work better compared to learned features during training of NNs... | SP:4e5cbc8389be556e7f0bc008d19d635e6736622f |
Differentially Private Learning Needs Better Features (or Much More Data) | 1 INTRODUCTION . Machine learning ( ML ) models have been successfully applied to the analysis of sensitive user data such as medical images ( Lundervold & Lundervold , 2019 ) , text messages ( Chen et al. , 2019 ) or social media posts ( Wu et al. , 2016 ) . Training these ML models under the framework of differential... | The paper considers ways of improving private versions of SGD in the context of image classification. The main finding is that providing "hand crafted" features can significantly improve the privacy/accuracy trade-off. In some cases, even a linear model built on top of such features (like those produced by ScatterNet),... | SP:4e5cbc8389be556e7f0bc008d19d635e6736622f |
Better Fine-Tuning by Reducing Representational Collapse | 1 INTRODUCTION . Pre-trained language models ( Radford et al. , 2019 ; Devlin et al. , 2018 ; Liu et al. , 2019 ; Lewis et al. , 2019 ; 2020 ) have been shown to capture a wide array of semantic , syntactic , and world knowledge ( Clark et al. , 2019 ) , and provide the defacto initialization for modeling most existing... | - This paper presents a simple but effective method rooted in trust region theory for fine-tuning pre-trained models without 'representational collapse'. Compared to previous methods (such as SMART by Jiang et al. (2019)), the newly proposed methods (R3F and R4F) are computationally simple while achieving more strong p... | SP:88108abfa920eda1a0766301bdfd70113f61f8b3 |
Better Fine-Tuning by Reducing Representational Collapse | 1 INTRODUCTION . Pre-trained language models ( Radford et al. , 2019 ; Devlin et al. , 2018 ; Liu et al. , 2019 ; Lewis et al. , 2019 ; 2020 ) have been shown to capture a wide array of semantic , syntactic , and world knowledge ( Clark et al. , 2019 ) , and provide the defacto initialization for modeling most existing... | The paper proposes a method for finetuning pre-trained models that ensures the generalization ability of the representation is maintained. The key innovation is that the computationally expensive ascent step in the mirror descent method of SMART can be replaced by simply injecting noise. The results support the hypothe... | SP:88108abfa920eda1a0766301bdfd70113f61f8b3 |
Disentangling Action Sequences: Discovering Correlated Samples | 1 INTRODUCTION . The basis of artificial intelligence is to understand and reason about the world based on a limited set of observations . Unsupervised disentanglement learning is highly desirable due to its similarity with the way we as human think . For instance , we can infer the movement of a running ball based on ... | In this submission, a common modelling assumption for unsupervised disentanglement is challenged: that the disentangled representation follows the independence structure of the underlying (data generating) factors. Instead, the paper proposes to consider *action sequences* which describe how datapoints are interrelated... | SP:be719de25d3d60635a9508fd610f2da3f4fd164d |
Disentangling Action Sequences: Discovering Correlated Samples | 1 INTRODUCTION . The basis of artificial intelligence is to understand and reason about the world based on a limited set of observations . Unsupervised disentanglement learning is highly desirable due to its similarity with the way we as human think . For instance , we can infer the movement of a running ball based on ... | This paper addresses the problem of disentangling representations using Variational Autoencoders. In particular, the authors introduce the concept of disentangling action sequences and propose the fractional variational autoencoder framework to disentangle them step-by-step. To this end, they analyze the inductive bias... | SP:be719de25d3d60635a9508fd610f2da3f4fd164d |
Meta-Model-Based Meta-Policy Optimization | Model-based reinforcement learning ( MBRL ) has been applied to meta-learning settings and has demonstrated its high sample efficiency . However , in previous MBRL for meta-learning settings , policies are optimized via rollouts that fully rely on a predictive model of an environment . Thus , its performance in a real ... | The paper concerns model-based meta-RL. It exploits the fact that meta-RL can be formulated as POMDP in which the task indicator is part of the (unobserved) hidden state. Thus, the paper effectively analyzes and proposes model-based algorithms for POMDPs. The paper bounds the gap between the expected reward of a policy... | SP:ad4ab0f3fa32fd60cf01ee69259206d6d6f4ed22 |
Meta-Model-Based Meta-Policy Optimization | Model-based reinforcement learning ( MBRL ) has been applied to meta-learning settings and has demonstrated its high sample efficiency . However , in previous MBRL for meta-learning settings , policies are optimized via rollouts that fully rely on a predictive model of an environment . Thus , its performance in a real ... | This paper focus on model-based RL on a POMDP setting (they call it "meta RL"), where the policy and model need to infer the current hidden state according to history. It provides a theoretical relation between true environment returns and the returns from learned models in a POMDP setting. And it also provides a pract... | SP:ad4ab0f3fa32fd60cf01ee69259206d6d6f4ed22 |
Does enhanced shape bias improve neural network robustness to common corruptions? | 1 INTRODUCTION . As deep learning is increasingly applied to open-world perception problems in safety-critical domains such as robotics and autonomous driving , its robustness properties become of paramount importance . Generally , a lack of robustness against adversarial examples has been observed ( Szegedy et al. , 2... | This paper delves deeper into understanding shape-based representation of CNNs in an empirical way. Based on the stylized images, it proposes to use edge maps to more explicitly feed shape information to learning models. Besides, the common way to let models learn the shape-based representation is to train on the datas... | SP:da04daf3c2ef194dd3e9460acf3c967bb0222062 |
Does enhanced shape bias improve neural network robustness to common corruptions? | 1 INTRODUCTION . As deep learning is increasingly applied to open-world perception problems in safety-critical domains such as robotics and autonomous driving , its robustness properties become of paramount importance . Generally , a lack of robustness against adversarial examples has been observed ( Szegedy et al. , 2... | The paper disproves the hypothesis that addressing shape bias improves robustness to corruptions of neural networks, which has been stated by the previous studies [1, 2]. The paper demonstrates that the degree of shape bias of a model is not correlated with classification accuracy on corrupted images via experiments. F... | SP:da04daf3c2ef194dd3e9460acf3c967bb0222062 |
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics | 1 INTRODUCTION . Imagine a ball rolling down a ramp . If asked to predict the trajectory of the ball , most of us will find it fairly easy to make a reasonable prediction . Not only that , simply by observing a single trajectory people can make reasonable guesses about the material and weight of the ball and the ramp .... | The paper proposes an Bayesian-symbolic physics (BSP), an intuitive physics model that jointly infers symbolic force laws and object properties (mass, friction coefficient). The inductive bias is force summation, F=ma, and a grammar of force laws to express object interactions. The inference is done via an EM method th... | SP:10461f5707fe6a701045ed1c3a96c22ceb858960 |
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics | 1 INTRODUCTION . Imagine a ball rolling down a ramp . If asked to predict the trajectory of the ball , most of us will find it fairly easy to make a reasonable prediction . Not only that , simply by observing a single trajectory people can make reasonable guesses about the material and weight of the ball and the ramp .... | The paper addresses the problem of sample-efficient inference for symbolic physical rules. In the literature, there exists neural-network based models for learning a physical engine which have good predictive accuracy but poor sample efficiency, as well as symbolic models which are highly sensitive to deviations from t... | SP:10461f5707fe6a701045ed1c3a96c22ceb858960 |
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning | 1 INTRODUCTION . Discovering causation in environments an agent might encounter remains an open and challenging problem for causal reinforcement learning ( Schölkopf ( 2015 ) , Bengio et al . ( 2013 ) , Schölkopf ( 2019 ) ) . Most approaches take the form of BAMDPs ( Bayes Adaptive Markov Decision Processes ) ( Zintgra... | This paper develops an intrinsic reward to help identify factors of variation within a family of MDPs. This intrinsic reward takes a form of curiosity and is used to develop initial behaviors to identify the causes of the latent variation in the environment dynamics. The experiments are used to validate the proposed in... | SP:5343a29c611b40fc6df160bff09a9aaf8140d0ab |
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning | 1 INTRODUCTION . Discovering causation in environments an agent might encounter remains an open and challenging problem for causal reinforcement learning ( Schölkopf ( 2015 ) , Bengio et al . ( 2013 ) , Schölkopf ( 2019 ) ) . Most approaches take the form of BAMDPs ( Bayes Adaptive Markov Decision Processes ) ( Zintgra... | This paper considers the problem of skill discovery in settings where the data appears to be a Markov Decision Process and part of the state is unobservable. The hidden state variables are interpreted as causal factors that control important aspects of the environment dynamics. Under this interpretation, the paper adv... | SP:5343a29c611b40fc6df160bff09a9aaf8140d0ab |
ECONOMIC HYPERPARAMETER OPTIMIZATION WITH BLENDED SEARCH STRATEGY | 1 INTRODUCTION . Hyperparameter optimization ( HPO ) of modern machine learning models is a resource-consuming task , which is unaffordable to individuals or organizations with little resource ( Yang & Shami , 2020 ) . Operating HPO in a low-cost regime has numerous benefits , such as democratizing ML techniques , enab... | The proposed BlendedSearch (BS) presents an intuitive next step in the combination of global and local search schemes for hyper-parameter optimization (HPO). Global search schemes are widely used for HPO but can suffer from large HPO times since their vanilla forms do not account for function evaluation costs. Local se... | SP:59f3aa13da7e04d36e60a67555cd8254047e949a |
ECONOMIC HYPERPARAMETER OPTIMIZATION WITH BLENDED SEARCH STRATEGY | 1 INTRODUCTION . Hyperparameter optimization ( HPO ) of modern machine learning models is a resource-consuming task , which is unaffordable to individuals or organizations with little resource ( Yang & Shami , 2020 ) . Operating HPO in a low-cost regime has numerous benefits , such as democratizing ML techniques , enab... | This paper proposes BlendSearch, which combines global and local optimisation for the problem of hyperparameter optimisation when search cost is heterogenous. To achieve so, they use the combination of one global search instance (e.g. Bayesian optimisation; used to identify promising regions as starting points for loca... | SP:59f3aa13da7e04d36e60a67555cd8254047e949a |
Deformable Capsules for Object Detection | 1 INTRODUCTION . Capsule networks promise many potential benefits over convolutional neural networks ( CNNs ) . These include practical benefits , such as requiring less data for training or better handling unbalanced class distributions ( Jiménez-Sánchez et al. , 2018 ) , and important theoretical benefits , such as b... | The paper proposes to use the capsules to perform object detection on COCO. Capsules, while showing promises, are usually too expensive for tasks beyond MNIST and Cifar. The authors propose three key improvements in DeformCaps, SplitCaps and SE-Routing to improve the efficiency and therefore allow capsules to be applie... | SP:ba5cdfc4c1ad55f08c3e39934785e11e61b202ea |
Deformable Capsules for Object Detection | 1 INTRODUCTION . Capsule networks promise many potential benefits over convolutional neural networks ( CNNs ) . These include practical benefits , such as requiring less data for training or better handling unbalanced class distributions ( Jiménez-Sánchez et al. , 2018 ) , and important theoretical benefits , such as b... | This paper introduces capsule network for object detection. To solve the issue of capsule network when applied to large-scale detection problems, this paper develops deformable capsules, a new prediction head SplitCaps, and a dynamic routing algorithm, SE-Routing. Experiments are conducted on COCO where it performs sl... | SP:ba5cdfc4c1ad55f08c3e39934785e11e61b202ea |
Learning Associative Inference Using Fast Weight Memory | 1 INTRODUCTION . Humans continually adapt in order to understand new situations in changing environments . One important adaptive ability is associative inference for composing features extracted from distinct experiences and relating them to each other ( Schlichting & Preston , 2015 ; Gershman et al. , 2015 ) . Suppos... | The authors present a working memory model composed of a recurrent neural network trained via gradient descent and an associative memory based on the approach taken by Ba et al. (2016) in "Using Fast Weights to Attend to the Recent Past". The model consists of an LSTM to which takes the input and its own state from th... | SP:38d522d92ad048087149a9d612a694c8ab95f3af |
Learning Associative Inference Using Fast Weight Memory | 1 INTRODUCTION . Humans continually adapt in order to understand new situations in changing environments . One important adaptive ability is associative inference for composing features extracted from distinct experiences and relating them to each other ( Schlichting & Preston , 2015 ; Gershman et al. , 2015 ) . Suppos... | The solution proposed is the combination of an RNN (LSTM) and Fast Weighted Memory (FWM). The LSTM produces a query to the memory used to retrieve information from the memory and be presented at the model output. It also controls the memory through fast weights that are updated through a Hebbian mechanism. The FWM is b... | SP:38d522d92ad048087149a9d612a694c8ab95f3af |
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds | 1 INTRODUCTION . Off-policy evaluation ( OPE ) seeks to estimate the expected reward of a target policy in reinforcement learnings ( RL ) from observational data collected under different policies ( e.g. , Murphy et al. , 2001 ; Fonteneau et al. , 2013 ; Jiang & Li , 2016 ; Liu et al. , 2018a ) . OPE plays a central ro... | The paper studies an off-policy evaluation (OPE) problem for Markov decision processes (MDPs). It suggests an optimization-based method that can construct a non-asymptotic confidence interval, for a given confidence level, for the value function of a policy starting from a fixed initial distribution. The paper builds o... | SP:22b6740eb3b2977aaffb8919aee4883f62af815f |
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds | 1 INTRODUCTION . Off-policy evaluation ( OPE ) seeks to estimate the expected reward of a target policy in reinforcement learnings ( RL ) from observational data collected under different policies ( e.g. , Murphy et al. , 2001 ; Fonteneau et al. , 2013 ; Jiang & Li , 2016 ; Liu et al. , 2018a ) . OPE plays a central ro... | This work constructs non-asymptotic confidence intervals for off-policy evaluation. This is achieved by assuming that the reward at any given time only depends on the state action pair, leveraging that assumed structure to define the difference between the empirical and estimated bellman residual operators as a Marting... | SP:22b6740eb3b2977aaffb8919aee4883f62af815f |
ROGA: Random Over-sampling Based on Genetic Algorithm | 1 INTRODUCTION . When modeling the classification problem , the balance of classes can ensure that the information is balanced during the learning process of the model , but the classes are often unbalanced in actual tasks ( Kotsiantis et al . ( 2005 ) ; He & Garcia ( 2009 ) ) , which leads to the machine learning mode... | The authors focus on the class imbalance problem and propose an algorithm named ROGA to generated samples of minority classes to balance the quantitative difference between classes. Specifically, the proposed ROGA generates samples of minority classes by using a Genetic Algorithm (GA) to explore the sample space. Moreo... | SP:dd6a80c29d23d8ee356c637fff15c8d28956fba3 |
ROGA: Random Over-sampling Based on Genetic Algorithm | 1 INTRODUCTION . When modeling the classification problem , the balance of classes can ensure that the information is balanced during the learning process of the model , but the classes are often unbalanced in actual tasks ( Kotsiantis et al . ( 2005 ) ; He & Garcia ( 2009 ) ) , which leads to the machine learning mode... | In this work, the authors propose an oversampling technique which creates a population of synthetic samples for an imbalanced dataset using genetic algorithms. Each individual in the GA population corresponds to a synthetic sample, and the fitness function is based on the similarity (in feature space) of the synthetic ... | SP:dd6a80c29d23d8ee356c637fff15c8d28956fba3 |
Reusing Preprocessing Data as Auxiliary Supervision in Conversational Analysis | 1 INTRODUCTION . The sharp increase in uses of video-conferencing creates both a need and an opportunity to better understand these conversations ( Kim et al. , 2019a ) . In post-event applications , analyzing conversations can give feedback to improve communication skills ( Hoque et al. , 2013 ; Naim et al. , 2015 ) .... | This paper addresses challenges faced in the multi-task learning (MTL) models used in analyzing multimodal conversational data. The main challenge paper is trying to solve is on how to select relevant auxiliary tasks that avoid negative transfer. The authors explore how the preprocessed data used for feature engineerin... | SP:14c8ee6cc91f94c22b9cd29c98be73e551677937 |
Reusing Preprocessing Data as Auxiliary Supervision in Conversational Analysis | 1 INTRODUCTION . The sharp increase in uses of video-conferencing creates both a need and an opportunity to better understand these conversations ( Kim et al. , 2019a ) . In post-event applications , analyzing conversations can give feedback to improve communication skills ( Hoque et al. , 2013 ; Naim et al. , 2015 ) .... | This paper studies how the preprocessed data can be reused as auxiliary tasks in primary multi-task learning (MTL) for the multimodal emotion detection task. The authors propose and test three hypotheses for primary MTL. Two different hierarchical-level models, FLAT-MTL hierarchical attention model and HAN-Rock model, ... | SP:14c8ee6cc91f94c22b9cd29c98be73e551677937 |
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data | 1 INTRODUCTION . Despite the recent success of deep neural networks with large-scale labeled data , many real-world scenarios suffer from expensive data acquisition and labeling costs . This has motivated the community to develop semi-supervised learning ( SSL ; Grandvalet & Bengio 2004 ; Chapelle et al . 2009 ) , i.e.... | The paper proposes a new approach for open set semi-supervised learning, where there are unlabeled data from classes not in the labeled data. The paper uses a contrastive representation learning paradigm to learn a feature encoder and a similarity measurement. Then the paper filters outlier samples by the similarity me... | SP:313f52f0734140154ab31a602457829ae6eda9c0 |
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data | 1 INTRODUCTION . Despite the recent success of deep neural networks with large-scale labeled data , many real-world scenarios suffer from expensive data acquisition and labeling costs . This has motivated the community to develop semi-supervised learning ( SSL ; Grandvalet & Bengio 2004 ; Chapelle et al . 2009 ) , i.e.... | This paper considers the problem of semi-supervised learning, where the unlabeled data may include out-of-class samples. To address this task, the paper proposes a method consisting of three steps: (1) detecting out-of-class samples in the unlabeled set, (2) assigning soft-labels to the detected out-of-class samples us... | SP:313f52f0734140154ab31a602457829ae6eda9c0 |
Unsupervised Object Keypoint Learning using Local Spatial Predictability | 1 INTRODUCTION . An intelligent agent situated in the visual world critically depends on a suitable representation of its incoming sensory information . For example , a representation that captures only information about relevant aspects of the world makes it easier to learn downstream tasks efficiently ( Barlow , 1989... | The authors tackle the problem of self-supervised representation learning, and validate their approach on downstream Reinforcement Learning tasks. Building on the insight that predictability in local patches is a good inductive bias for salient regions that characterize objects, the authors propose a well-reasoned, wel... | SP:794393405d88536ffc86021bf4939b168ff7f791 |
Unsupervised Object Keypoint Learning using Local Spatial Predictability | 1 INTRODUCTION . An intelligent agent situated in the visual world critically depends on a suitable representation of its incoming sensory information . For example , a representation that captures only information about relevant aspects of the world makes it easier to learn downstream tasks efficiently ( Barlow , 1989... | This paper works on unsupervised discovering keypoints in an Atari game frame to help improving Atari game performance. The keypoint discovery is based on predicting "predictable" local structure. I.e., the authors consider points that can not be predicted from its neighbor as good. Experiments show the learned keypoin... | SP:794393405d88536ffc86021bf4939b168ff7f791 |
Do Transformers Understand Polynomial Simplification? | Recently researchers have demonstrated that Transformers can be trained to learn symbolic tasks such as solving integration and differential equations in an end-toend fashion . In these setups , for an input symbolic expression , the Transformer predicts the final solution in a single step . Since such tasks may consis... | The paper "Do Transformers Understand Polynomial Simplification?" introduces a new reasoning task (convert polynomials into a normal form) and studies the performance and errors of Transformers on this task. The task itself is quite simple: given a randomly generated term involving small constants, variables, additions... | SP:37921395ed7b214f2921391b2fde1f2ba209719f |
Do Transformers Understand Polynomial Simplification? | Recently researchers have demonstrated that Transformers can be trained to learn symbolic tasks such as solving integration and differential equations in an end-toend fashion . In these setups , for an input symbolic expression , the Transformer predicts the final solution in a single step . Since such tasks may consis... | The authors analyze the performance of Transformer models on simplifying polynomials and - importantly - generating proofs at the same time. This is a very nice idea that allows to study the performance of Transformers in depth and at the same time in an important setting where verification is performed as part of runn... | SP:37921395ed7b214f2921391b2fde1f2ba209719f |
Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channel | 1 INTRODUCTION . The Graphics Processing Unit ( GPU ) is a favored vehicle for executing a neural network . As it computes , it also hums—electromagnetically . What can this hum tell us ? Could listening to the GPU ’ s electromagnetic ( EM ) radiation reveal details about the neural network ? We study this question and... | The paper presents a method for capturing the shape (type of layers) and their respective parameters of a neural network through the magnetic field induced as the GPU drains power. In particular, the GPU is snooped using an off-the-shelf magnetic induction sensor which is placed along the power cable of the GPU. It tur... | SP:f71ceed51963fee2042d66da98c14aeb91b93f74 |
Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channel | 1 INTRODUCTION . The Graphics Processing Unit ( GPU ) is a favored vehicle for executing a neural network . As it computes , it also hums—electromagnetically . What can this hum tell us ? Could listening to the GPU ’ s electromagnetic ( EM ) radiation reveal details about the neural network ? We study this question and... | This paper demonstrates that magnetic side channel information from a GPU (that is processing a deep neural net) can be snooped to recover the architecture and hyperparameters of the neural network. While the concept of side channel information snooping to recover codes/software (including ML models) is widely studied,... | SP:f71ceed51963fee2042d66da98c14aeb91b93f74 |
Efficient Sampling for Generative Adversarial Networks with Reparameterized Markov Chains | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have achieved a great success on generating realistic images in recent years ( Karras et al. , 2019 ; Brock et al. , 2019 ) . Unlike previous models that explicitly parameterize the data distribution , GANs rely on an alternative opt... | The paper proposes an MCMC based sampling mechanism for GANs. In contrast to earlier work, the proposal distribution is conditioning conditioned on the previous state (here in latent space), which is supposed to help sampling efficiency. This is achieved by a clever re-parametrization of intermediate steps of the MCMC ... | SP:93f8114b248a8fbae75eadc40d70c6d38f3faff4 |
Efficient Sampling for Generative Adversarial Networks with Reparameterized Markov Chains | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have achieved a great success on generating realistic images in recent years ( Karras et al. , 2019 ; Brock et al. , 2019 ) . Unlike previous models that explicitly parameterize the data distribution , GANs rely on an alternative opt... | The paper proposes an MCMC sampling strategy for GANs. The idea is clear: for high-dimensional x, making a good proposal is difficult, so they propose to do that in the latent space. Then they use a similar strategy as MH-GAN to compute a rejection strategy. The difference between the two methods is that proposal in M... | SP:93f8114b248a8fbae75eadc40d70c6d38f3faff4 |
On the Predictability of Pruning Across Scales | 1 INTRODUCTION . For decades , neural network pruning—eliminating unwanted parts of the network—has been a popular approach for reducing network sizes or computational demands of inference ( LeCun et al. , 1990 ; Reed , 1993 ; Han et al. , 2015 ) . In practice , pruning can reduce the parameter-counts of contemporary m... | This paper studies how to estimate the performance of pruned networks using regression models. The authors first empirically observe that there exist three distinct regions of sparsity: (1) In the low-sparsity regime, pruning does not decrease the accuracy (2) In the mid-sparsity regime, a linear relationship between t... | SP:dac2e985d39e3466dafbf20124fdafe0f5b9bd24 |
On the Predictability of Pruning Across Scales | 1 INTRODUCTION . For decades , neural network pruning—eliminating unwanted parts of the network—has been a popular approach for reducing network sizes or computational demands of inference ( LeCun et al. , 1990 ; Reed , 1993 ; Han et al. , 2015 ) . In practice , pruning can reduce the parameter-counts of contemporary m... | The authors propose a functional approximation to the error of pruned convolutional neural networks as a function of network hyperparameters. This functional approximation depends on a number of hyperparameters that are fit on the error of already trained and pruned networks on a certain task (in this case, image class... | SP:dac2e985d39e3466dafbf20124fdafe0f5b9bd24 |
MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement | 1 INTRODUCTION . The importance of scalable health sensing has been acutely highlighted during the SARS-CoV-2 ( COVID-19 ) pandemic . The virus has been linked to increased risk of myocarditis and other serious cardiac ( heart ) conditions ( Puntmann et al. , 2020 ) . Contact sensors ( electrocardiograms , oximeters ) ... | In this paper, the authors present a few-shot learning model for non-contact physiological signal measurement to build a more accurate and convenient personalized health sensing system. The motivation is that traditional fine-tuning method for this task is difficult since it requires large sets of high-quality training... | SP:30580fb0f3acf76221f8b031518a30228c4d6162 |
MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement | 1 INTRODUCTION . The importance of scalable health sensing has been acutely highlighted during the SARS-CoV-2 ( COVID-19 ) pandemic . The virus has been linked to increased risk of myocarditis and other serious cardiac ( heart ) conditions ( Puntmann et al. , 2020 ) . Contact sensors ( electrocardiograms , oximeters ) ... | The authors propose a system called MetaPhys for personalized remote physiological sensing from videos. Their system combines a pre-trained CNN with an existing meta learning method (MAML). The investigated both supervised and unsupervised training of their system. Performance evaluation of their methods on benchmark... | SP:30580fb0f3acf76221f8b031518a30228c4d6162 |
Stochastic Subset Selection for Efficient Training and Inference of Neural Networks | Current machine learning algorithms are designed to work with huge volumes of high dimensional data such as images . However , these algorithms are being increasingly deployed to resource constrained systems such as mobile devices and embedded systems . Even in cases where large computing infrastructure is available , ... | This paper proposes a stochastic subset selection method for reducing the storage / transmission cost of datasets. The proposes method minimizes the expected loss over selected datasets. The data selection algorithm consists a candidate selection stage and an autoregressive selection stage, parameterized with neural ne... | SP:adf55a0c96d1e5ffb8016b8bec41aa0caca79793 |
Stochastic Subset Selection for Efficient Training and Inference of Neural Networks | Current machine learning algorithms are designed to work with huge volumes of high dimensional data such as images . However , these algorithms are being increasingly deployed to resource constrained systems such as mobile devices and embedded systems . Even in cases where large computing infrastructure is available , ... | This work introduces a method to select instances from any set (stochastic subset selection, or SSS). The experiments demonstrate a diverse set of use-cases, including feature selection and core-set selection. The proposed approach is a two-stage method involving candidate selection (learning a function $\rho$ to deter... | SP:adf55a0c96d1e5ffb8016b8bec41aa0caca79793 |
On the Dynamic Regret of Online Multiple Mirror Descent | 1 INTRODUCTION . Online optimization refers to the design of sequential decisions where system parameters and cost functions vary with time . It has applications to various classes of problems , such as object tracking ( Shahrampour & Jadbabaie , 2017 ) , networking ( Shi et al. , 2018 ) , cloud computing ( Lin et al. ... | This paper studies the dynamic regret of online multiple mirror descent, which is online mirror descent with M repeated steps on each of T sequential loss functions. The authors show three bounds for the dynamic regret of OMMD, which generalizes OMGD [Zhang et al. '17]: C_T (the path length of the minimizer sequence), ... | SP:874a441d5c5c7582a1d548bc5d0c635ed032434f |
On the Dynamic Regret of Online Multiple Mirror Descent | 1 INTRODUCTION . Online optimization refers to the design of sequential decisions where system parameters and cost functions vary with time . It has applications to various classes of problems , such as object tracking ( Shahrampour & Jadbabaie , 2017 ) , networking ( Shi et al. , 2018 ) , cloud computing ( Lin et al. ... | This work derives a new upper bound on the dynamic regret for online convex optimization in the restricted setting where the comparison sequence is made up of the minimizers x^_1,...,x*_T of the loss sequence. There are three main parameters that control regret in this case: the path length C_T = ||x*_2-x*_1||+...+||x*... | SP:874a441d5c5c7582a1d548bc5d0c635ed032434f |
Learning perturbation sets for robust machine learning | 1 INTRODUCTION . Within the last decade , adversarial learning has become a core research area for studying robustness and machine learning . Adversarial attacks have expanded well beyond the original setting of imperceptible noise to more general notions of robustness , and can broadly be described as capturing sets o... | .** In this work, the author(s) have presented an approach to identify valid perturbation operations that can be applied to the model inputs, which can be exploited to boost model robustness via purposefully corrupting the inputs during training. A weakly supervised setting has been assumed, such that pairs of valid pe... | SP:8c174c4e2b083f7313ad07aabbb7a3520d017499 |
Learning perturbation sets for robust machine learning | 1 INTRODUCTION . Within the last decade , adversarial learning has become a core research area for studying robustness and machine learning . Adversarial attacks have expanded well beyond the original setting of imperceptible noise to more general notions of robustness , and can broadly be described as capturing sets o... | This paper addresses the problem of constraining adversarial image perturbations to be similar to some natural class of perturbations. This would allow natural perturbations to be treated with the same rigor (e.g. quantifiable attack and defense strengths) as standard adversarial perturbations. Standard adversarial per... | SP:8c174c4e2b083f7313ad07aabbb7a3520d017499 |
SEED: Self-supervised Distillation For Visual Representation | 1 INTRODUCTION The burgeoning studies and success on self-supervised learning ( SSL ) for visual representation are mainly marked by its extraordinary potency of learning from unlabeled data at scale . Accompanying with the SSL is its phenomenal benefit of obtaining task-agnostic representations while allowing the trai... | The paper address the problem of knowledge distillation in self-supervised learning, where the representational knowledge from the larger model (i.e., teacher) is used to guide the learning of a smaller model (i.e, student). To achieve this, an instance queue is used to compute the similarity score between teacher mode... | SP:9f8b8c56abc19f30f03426367ab036ba47bc1f27 |
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