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Effective Regularization Through Loss-Function Metalearning | Loss-function metalearning can be used to discover novel , customized loss functions for deep neural networks , resulting in improved performance , faster training , and improved data utilization . A likely explanation is that such functions discourage overfitting , leading to effective regularization . This paper demo... | Taylor polynomial based loss function metalearning acts as a regularizer that improves the networks adversarial attack robustness, performance, training time, and data utilization. The authors evolve weights, and so add arbitrary other factors to the loss, including adversarial robustness to learn a loss function param... | SP:3021e8b74146a257b5befec014cd17d5d7bd3362 |
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning | 1 INTRODUCTION . Many pervasive deep reinforcement learning ( RL ) algorithms estimate value functions using bootstrapping , that is , by sequentially fitting value functions to target value estimates generated from the value function learned in the previous iteration . Despite high-profile achievements ( Silver et al.... | This paper identifies a type of implicit under-parameterization phenomenon in deep RL methods that use bootstrapping. It is found that after an initial learning period, the effective rank of the feature matrix keeps decreasing. This implies that the representational power of the network is not fully utilized. The auth... | SP:22eafda74c0a1a8184893a5ed47a36cfab1c361e |
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning | 1 INTRODUCTION . Many pervasive deep reinforcement learning ( RL ) algorithms estimate value functions using bootstrapping , that is , by sequentially fitting value functions to target value estimates generated from the value function learned in the previous iteration . Despite high-profile achievements ( Silver et al.... | This paper discusses a phenomenon wherein the feature vectors of the learned value function in reinforcement learning (RL) lose their diversity as training progresses. The paper analyzes the rank of the final hidden layer in the model parameterizing the value function and shows experimentally that for offline-RL and on... | SP:22eafda74c0a1a8184893a5ed47a36cfab1c361e |
Certified Watermarks for Neural Networks | 1 INTRODUCTION . With the rise of deep learning , there has been an extraordinary growth in the use of neural networks in various computer vision and natural language understanding tasks . In parallel with this growth in applications , there has been exponential growth in terms of the cost required to develop and train... | The proposed method exploits the randomized smoothing techniques for a certified watermark of neural networks. The idea itself is novel and interesting. To the best of the reviewer's knowledge, no one has ever used randomized smoothing for neural network watermark. Different from the defense against adversarial example... | SP:00a3a2ca6324e5f2db45bbad11bf97491037c26c |
Certified Watermarks for Neural Networks | 1 INTRODUCTION . With the rise of deep learning , there has been an extraordinary growth in the use of neural networks in various computer vision and natural language understanding tasks . In parallel with this growth in applications , there has been exponential growth in terms of the cost required to develop and train... | This paper present the first certifiable neural network watermark method. By extending method proposed by Chiang et.al. [1] to the watermark embedding and extraction process, it is possible to ensure that the watermark is robust to watermark removal when the network parameters are modified by less than a certain calcul... | SP:00a3a2ca6324e5f2db45bbad11bf97491037c26c |
A Geometric Analysis of Deep Generative Image Models and Its Applications | 1 BACKGROUND . Generative adversarial networks ( GANs ) learn patterns that characterize complex datasets , and subsequently generate new samples representative of that set . In recent years , there has been tremendous success in training GANs to generate high-resolution and photorealistic images ( Karras et al. , 2017... | This work intends to explore the geometry of the latent space and proposes to define the distance in latent space as the distance between the corresponding generated images and use the Hessian of that squared distance as metric tensor to define the manifold. Using Learned Perceptual Image Patch Similarity (LPIPS), they... | SP:80d8f2ef8e3b7ad7f8407b80f29c70111d80e22e |
A Geometric Analysis of Deep Generative Image Models and Its Applications | 1 BACKGROUND . Generative adversarial networks ( GANs ) learn patterns that characterize complex datasets , and subsequently generate new samples representative of that set . In recent years , there has been tremendous success in training GANs to generate high-resolution and photorealistic images ( Karras et al. , 2017... | The paper performs the analysis of the GAN latent spaces from the geometric perspective, inducing a metric tensor in the latent space from the LPIPS distance in the image space. The main authors' finding is that under such metric, the latent spaces of typical GANs are highly anisotropic, which can be exploited for more... | SP:80d8f2ef8e3b7ad7f8407b80f29c70111d80e22e |
What are the Statistical Limits of Offline RL with Linear Function Approximation? | 1 INTRODUCTION . Offline methods ( also known as off-policy methods or batch methods ) are a promising methodology to alleviate the sample complexity burden in challenging reinforcement learning ( RL ) settings , particularly those where sample efficiency is paramount ( Mandel et al. , 2014 ; Gottesman et al. , 2018 ; ... | The paper provides a theoretical analysis on the sample complexity of OPE with linear function approximation and assumptions on representation and distribution shift. The main results shows that realizability and feature coverage are not sufficient to guarantee a polynomial sample complexity. The paper also provides a ... | SP:32d66326d021e4868f893849d6628d7c86da55a9 |
What are the Statistical Limits of Offline RL with Linear Function Approximation? | 1 INTRODUCTION . Offline methods ( also known as off-policy methods or batch methods ) are a promising methodology to alleviate the sample complexity burden in challenging reinforcement learning ( RL ) settings , particularly those where sample efficiency is paramount ( Mandel et al. , 2014 ; Gottesman et al. , 2018 ; ... | This paper presents a impossibility result for value-function approximation in batch-mode RL. The chart below puts this work in context. This work essentially shows -- through a constructive example of an MDP -- that the amount of data needed for approximating Q values must increases exponentially with the horizon in e... | SP:32d66326d021e4868f893849d6628d7c86da55a9 |
Dynamic Graph Representation Learning with Fourier Temporal State Embedding | 1 INTRODUCTION . Graph Representation Learning learns the graphs with low-dimensional vectors at nodes and graphs level . ( Perozzi et al. , 2014 ; Tang et al. , 2015 ; Wang et al. , 2016 ; Cao et al. , 2015 ; Ou et al. , 2016 ) In recent years , deep neural networks ( DNNs ) are extended to process graphical data and ... | The article presents a new approach for learning representations of dynamic graphs. The method is based on Fourier Transform of edges over time, and separate GCNs are used for each Fourier mode (N FTSE) to stain temporal embeddings. Numerical results illustrate the performance of the method on there graph tasks, namely... | SP:18e3aa22c55669fd93fff33b31cb023cc5a4e9f6 |
Dynamic Graph Representation Learning with Fourier Temporal State Embedding | 1 INTRODUCTION . Graph Representation Learning learns the graphs with low-dimensional vectors at nodes and graphs level . ( Perozzi et al. , 2014 ; Tang et al. , 2015 ; Wang et al. , 2016 ; Cao et al. , 2015 ; Ou et al. , 2016 ) In recent years , deep neural networks ( DNNs ) are extended to process graphical data and ... | This paper presents a new method called Fourier temporal state embedding. The motivation for this approach is unclear and should be appropriately justified. In the abstract and introduction, the claim appears to be that previous methods are not time nor memory-efficient, and therefore FTSE is proposed. But this is obvi... | SP:18e3aa22c55669fd93fff33b31cb023cc5a4e9f6 |
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification | 1 INTRODUCTION . Reinforcement Learning ( RL ) has had a number of recent successes in various application domains which include computer games ( Silver et al. , 2017 ; Mnih et al. , 2015 ; Tessler et al. , 2017 ) and robotics ( Abdolmaleki et al. , 2018a ) . As RL and deep learning continue to scale , an increasing nu... | The paper suggests two approaches to combine the concepts of robust Markov decision processes (MDPs) with that of constrained MDPs. In the first approach, called R3C, a worst-case setting is used for both the expected total discounted rewards criterion and the constraints on the state-action pairs. The robustness is de... | SP:9bc5be7060804da42581342ed69d6cd6cf2c90f3 |
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification | 1 INTRODUCTION . Reinforcement Learning ( RL ) has had a number of recent successes in various application domains which include computer games ( Silver et al. , 2017 ; Mnih et al. , 2015 ; Tessler et al. , 2017 ) and robotics ( Abdolmaleki et al. , 2018a ) . As RL and deep learning continue to scale , an increasing nu... | The standard Reinforcement Learning framework is limited in many ways, and numerous variants have been introduced to deal with aspects such as partial observability, temporal abstraction, safety, domain transfer, etc. Yet, these issues are often studied separately and it is often unclear how to combine them together. T... | SP:9bc5be7060804da42581342ed69d6cd6cf2c90f3 |
Multi-EPL: Accurate Multi-source Domain Adaptation | Given multiple source datasets with labels , how can we train a target model with no labeled data ? Multi-source domain adaptation ( MSDA ) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels . MSDA is a crucial problem applicable to many practical c... | This paper studies the multi-source domain adaptation (MSDA) problem. The authors argue that the existing MSDA solutions (1) do not explicitly consider distribution conditioned on labels of each domain, (2) rely on limited feature extraction based on one extractor, (3) do not well explore target data due to the absence... | SP:6eb1eee13155a89e5122099295567d3bbe403b30 |
Multi-EPL: Accurate Multi-source Domain Adaptation | Given multiple source datasets with labels , how can we train a target model with no labeled data ? Multi-source domain adaptation ( MSDA ) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels . MSDA is a crucial problem applicable to many practical c... | The authors propose a novel method for multi-source domain adaptation (MSDA). For effective adaptation, the proposed method adopts three techniques: (1) label-wise moment matching, (2) pseudo-labeling target data, and (3) ensembling multiple feature extractors. Experimental results show that the proposed method outperf... | SP:6eb1eee13155a89e5122099295567d3bbe403b30 |
Overfitting for Fun and Profit: Instance-Adaptive Data Compression | 1 INTRODUCTION . The most common approach to neural lossy compression is to train a variational autoencoder ( VAE ) like model on a training dataset to minimize the expected RD cost D + βR ( Theis et al. , 2017 ; Kingma & Welling , 2013 ) . Although this approach has proven to be very successful ( Ballé et al. , 2018 ... | The paper describes an instance specific finetuning method for image and video compression including finetuning the decoder. Based on the shown experiments, the required additional bits for sending the updated finetuned model parameters are worth the achieved increase in RD performance. However, the method has only bee... | SP:668a13137e2d0f2dd7e7f9de5e72118d9a7eb5df |
Overfitting for Fun and Profit: Instance-Adaptive Data Compression | 1 INTRODUCTION . The most common approach to neural lossy compression is to train a variational autoencoder ( VAE ) like model on a training dataset to minimize the expected RD cost D + βR ( Theis et al. , 2017 ; Kingma & Welling , 2013 ) . Although this approach has proven to be very successful ( Ballé et al. , 2018 ... | This paper investigates how to improve the test time performance of learned image compression models through finetuning of the full model. The authors finetune the model (both the model parameters and the prior on the latent space) for every test-time instance, appending the model updates to the bitstream. The model up... | SP:668a13137e2d0f2dd7e7f9de5e72118d9a7eb5df |
Revisiting Point Cloud Classification with a Simple and Effective Baseline | 1 INTRODUCTION . Processing 3D point cloud data accurately is crucial in many applications including autonomous driving ( Navarro-Serment et al. , 2010 ) and robotics ( Rusu et al. , 2009 ) . In these settings , sensors like LIDAR produce unordered sets of points that correspond to object surfaces . Correctly classifyi... | In this paper, the author(s) do a careful analysis on the classification performance of various modern point cloud processing networks and show empirically that with evaluation protocol set the same for different models, PointNet++, which is a relatively old model, has similar or better performance than newly proposed ... | SP:22f0d88501a4d3cbaed9e347b94d600da992876f |
Revisiting Point Cloud Classification with a Simple and Effective Baseline | 1 INTRODUCTION . Processing 3D point cloud data accurately is crucial in many applications including autonomous driving ( Navarro-Serment et al. , 2010 ) and robotics ( Rusu et al. , 2009 ) . In these settings , sensors like LIDAR produce unordered sets of points that correspond to object surfaces . Correctly classifyi... | This paper discusses several protocols including data augmentation, point distribution, loss function, ensemble scheme, and testing models, which serves as a kindly reminder that the training protocol matters. As claimed, earlier work like PointNet++ can still achieve comparable performance to more recent methods. Such... | SP:22f0d88501a4d3cbaed9e347b94d600da992876f |
Reducing Implicit Bias in Latent Domain Learning | A fundamental shortcoming of deep neural networks is their specialization to a single task and domain . While recent techniques in multi-domain learning enable the learning of more domain-agnostic features , their success relies firmly on the presence of domain labels , typically requiring manual annotation and careful... | The authors propose a method for latent domain learning, where input data come from different domains and the domain labels are unknown. The proposed method consists of two parts: dynamic residual adapter and weighted domain transfer. The dynamic residual adapter acts as a mixture of expert layer. And the weighted doma... | SP:9c54758e7833e0c193817bdf1d1fa5b875902153 |
Reducing Implicit Bias in Latent Domain Learning | A fundamental shortcoming of deep neural networks is their specialization to a single task and domain . While recent techniques in multi-domain learning enable the learning of more domain-agnostic features , their success relies firmly on the presence of domain labels , typically requiring manual annotation and careful... | The paper describes dynamic residual adapters designed to adaptively account for latent domains, and weighted domain transfer. This framework injects adaptivity into networks, preventing them from overfitting to the largest domains in distributions, a failure mode of traditional models that are exposed in latent domain... | SP:9c54758e7833e0c193817bdf1d1fa5b875902153 |
Redesigning the Classification Layer by Randomizing the Class Representation Vectors | 1 INTRODUCTION . Deep learning models achieved breakthroughs in classification tasks , allowing setting state-of-theart results in various fields such as speech recognition ( Chiu et al. , 2018 ) , natural language processing ( Vaswani et al. , 2017 ) , and computer vision ( Huang et al. , 2017 ) . In image classificat... | This paper proposed a classification layer by randomizing the class representation vectors. This paper first analyses the class vector distributions between different training strategies, and then proposed the randomized class vector to improve the representation learning performance. The proposed model is further exte... | SP:7ce03d70aa54266baea251240b597fb9e967f084 |
Redesigning the Classification Layer by Randomizing the Class Representation Vectors | 1 INTRODUCTION . Deep learning models achieved breakthroughs in classification tasks , allowing setting state-of-theart results in various fields such as speech recognition ( Chiu et al. , 2018 ) , natural language processing ( Vaswani et al. , 2017 ) , and computer vision ( Huang et al. , 2017 ) . In image classificat... | This paper introduces a new approach to learn a multi-class image classification model by fixing the weights of the classification layer. The authors propose to draw the class vectors randomly and set them as fixed during training instead of training them. They analyze this approach when a model is trained with a categ... | SP:7ce03d70aa54266baea251240b597fb9e967f084 |
Bypassing the Random Input Mixing in Mixup | Mixup and its variants have promoted a surge of interest due to their capability of boosting the accuracy of deep models . For a random sample pair , such approaches generate a set of synthetic samples through interpolating both the inputs and their corresponding one-hot labels . Current methods either interpolate rand... | This paper simply combines mixup and self-distillation to achieve more adaptive soft label, which effectively regularize the training. In the manuscript, authors argue that the existed mixup-based approaches has two mainly efforts, may create misleading training samples or meet computation cost issue on creating sample... | SP:d56d16dbf3be1381b4deb4fe29892f894dff3ff4 |
Bypassing the Random Input Mixing in Mixup | Mixup and its variants have promoted a surge of interest due to their capability of boosting the accuracy of deep models . For a random sample pair , such approaches generate a set of synthetic samples through interpolating both the inputs and their corresponding one-hot labels . Current methods either interpolate rand... | The previous advanced Mixup methods, such as CutMix and PuzzleMix, involve input mixing. This paper suggests a new Mixup approach, called LaMix, that does not require input mixing. The solution is combining the original target label (interpolation of two one-hot targets) and generated target labels from an additional n... | SP:d56d16dbf3be1381b4deb4fe29892f894dff3ff4 |
Graph Joint Attention Networks | 1 INTRODUCTION . Many real-world data can be modeled as a graph , where a set of nodes ( vertices ) , edges , and bag-ofwords features respectively represent data instances , instance-instance interrelationships , and contents characterizing the nodes . For example , scientific articles in a research domain can be mode... | This paper proposes Graph Joint Attention Networks (JAT), which augment Graph Attention Networks (GAT) by introducing structural attention coefficients, which are combined with the feature based attention coefficients computed by GAT. The authors present two ways of incorporating the structural coefficients, namely Imp... | SP:dee5207ad69f2330fc0990728f00711848dc0067 |
Graph Joint Attention Networks | 1 INTRODUCTION . Many real-world data can be modeled as a graph , where a set of nodes ( vertices ) , edges , and bag-ofwords features respectively represent data instances , instance-instance interrelationships , and contents characterizing the nodes . For example , scientific articles in a research domain can be mode... | Creating aggregation weight over neighbor nodes lies at the key part of graph neural networks. Generally the weights can be generated by the node structure or feature similarity. The node structure similarity provides a way to measure the correlation of a pair of nodes with a complete graph. While attention weight usua... | SP:dee5207ad69f2330fc0990728f00711848dc0067 |
Neural Pooling for Graph Neural Networks | Tasks such as graph classification , require graph pooling to learn graph-level representations from constituent node representations . In this work , we propose two novel methods using fully connected neural network layers for graph pooling , namely Neural Pooling Method 1 and 2 . Our proposed methods have the ability... | This paper proposes two fully-connected layers based neural graph pooling methods for graph neural networks, named Neural Pooling Method 1 and Neural Pooling Method 2. The first method uses a first FC to reduce the feature dimension and then FC2 to compute the weights to do weighted-average over features for different ... | SP:445fc2caa1539447d3820e6ad6ae965d40d0cb76 |
Neural Pooling for Graph Neural Networks | Tasks such as graph classification , require graph pooling to learn graph-level representations from constituent node representations . In this work , we propose two novel methods using fully connected neural network layers for graph pooling , namely Neural Pooling Method 1 and 2 . Our proposed methods have the ability... | In this paper, the authors proposed two graph pooling methods, i.e., Neural Pooling Method 1 and 2. Both of them are flat pooling strategies, which try to obtain a graph representation directly from its node representations without coarsening graphs step by step. Specifically, the major idea of Neural Pooling Method 1 ... | SP:445fc2caa1539447d3820e6ad6ae965d40d0cb76 |
Risk-Averse Offline Reinforcement Learning | 1 INTRODUCTION . In high-stakes applications , the deployment of highly-performing Reinforcement Learning ( RL ) agents is limited by prohibitively large costs at early exploration stages ( Dulac-Arnold et al. , 2019 ) . To address this issue , the offline ( or batch ) RL setting considers learning a policy from a limi... | The authors propose an RL algorithm for learning risk-averse policies from offline data. Empirically, it is shown that it can outperform some existing risk-neutral approaches on a number of challenging robotic control tasks under risk-sensitive performance measures. Although the empirical results are encouraging, the t... | SP:aebd056143c988e81577e5604b38712895fa21ea |
Risk-Averse Offline Reinforcement Learning | 1 INTRODUCTION . In high-stakes applications , the deployment of highly-performing Reinforcement Learning ( RL ) agents is limited by prohibitively large costs at early exploration stages ( Dulac-Arnold et al. , 2019 ) . To address this issue , the offline ( or batch ) RL setting considers learning a policy from a limi... | The paper studies the problem of safe reinforcement learning, where we want to learn risk-averse policies in the offline setting. It proposes “Offline Risk Averse Actor Critic” (ORAAC) which performs competitively as risk-neural agent, and outperforms D4PG based baseline as a risk-averse agent on D4RL benchmark. The al... | SP:aebd056143c988e81577e5604b38712895fa21ea |
Disentangled cyclic reconstruction for domain adaptation | The domain adaptation problem involves learning a unique classification or regression model capable of performing on both a source and a target domain . Although the labels for the source data are available during training , the labels in the target domain are unknown . An effective way to tackle this problem lies in e... | This paper studies the domain adaptation problem by addressing the challenge of splitting task-specific and task-orthogonal information in the target domain using the proposed disentangled cyclic reconstruction method. The authors further develop a variant for the unsupervised domain adaption (UDA) task. The authors ar... | SP:d02cf08c6b78934d9bceb1fced353db460da192b |
Disentangled cyclic reconstruction for domain adaptation | The domain adaptation problem involves learning a unique classification or regression model capable of performing on both a source and a target domain . Although the labels for the source data are available during training , the labels in the target domain are unknown . An effective way to tackle this problem lies in e... | This paper proposes a new framework for unsupervised domain adaptation by applying the disentangled representations learning (DiCyR). The core idea of DiCyR is to split the raw feature into the task-related one and its complimentary context where the task-related representations are projected into a shared space for al... | SP:d02cf08c6b78934d9bceb1fced353db460da192b |
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission | 1 INTRODUCTION . The ability to provide precise performance guarantees is critical to many classification tasks ( Amodei et al. , 2016 ; Jiang et al. , 2012 ; 2018 ) . Yet , achieving perfect accuracy with only single guesses is often out of reach due to noise , limited data , insufficient modeling capacity , or other ... | This paper presents two advances in conformal prediction, a field with information retrieval applications in which a set of candidate responses to a query is presented and the objective is to return a small set of responses with at least one of the responses being the correct response. The first contribution is a meth... | SP:c895519de92206da36297207d000246430963b08 |
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission | 1 INTRODUCTION . The ability to provide precise performance guarantees is critical to many classification tasks ( Amodei et al. , 2016 ; Jiang et al. , 2012 ; 2018 ) . Yet , achieving perfect accuracy with only single guesses is often out of reach due to noise , limited data , insufficient modeling capacity , or other ... | Conformal prediction (CP) allows for the selection of a set of candidate answers guaranteed to contain the correct answer with some probability. The authors propose two extensions to CP, 1. To extend validity for all admissible answers, 2. Using prediction cascades to improve computational efficiency. The authors show ... | SP:c895519de92206da36297207d000246430963b08 |
On the Importance of Distraction-Robust Representations for Robot Learning | 1 INTRODUCTION . Representation Learning techniques form an integral part in many Reinforcement Learning ( RL ) robot control applications ( Lesort et al. , 2018 ) . Utilising low-dimensional representations can allow for a faster and more efficient learning of tasks than when using high-dimensional sensor information ... | Distractions are alterations of states or observations outside the control of the agent. Conventional learning methods tend to be quite sensitive to them. This work proposes a method for learning representations robust to such distractions using an encoder-decoder architecture whose encoder uses actions as input, ins... | SP:f1be80ff3839e9408ac1c693412c93d4f2483b95 |
On the Importance of Distraction-Robust Representations for Robot Learning | 1 INTRODUCTION . Representation Learning techniques form an integral part in many Reinforcement Learning ( RL ) robot control applications ( Lesort et al. , 2018 ) . Utilising low-dimensional representations can allow for a faster and more efficient learning of tasks than when using high-dimensional sensor information ... | This paper approaches the problem of representation learning for robotic learning, with a focus on being robust to potentially misleading distractor objects. Concretely, they propose a method which learns a representation of sequences of actions by reconstructing a future state, and uses this representation of actions ... | SP:f1be80ff3839e9408ac1c693412c93d4f2483b95 |
Topic-aware Contextualized Transformers | 1 INTRODUCTION . Language models ( LMs ) play an important role across a range of natural language processing tasks , such as text summarization ( Rush et al. , 2015 ; Gehrmann et al. , 2018 ) , neural machine translation ( NMT ) ( Sutskever et al. , 2014 ; Cho et al. , 2014a ) , and image captioning ( Herdade et al. ,... | This paper introduces an interesting idea of enhancing the contextualised word embedding learned by Transformers with long-range semantic dependencies via topic learned by Poisson Gamma Belief Network (PGBN), a deep topic model. To leverage the topic information to guide the learning process of transformers, the author... | SP:684f590cdc9c5592b1f8d815d4b380186697d37d |
Topic-aware Contextualized Transformers | 1 INTRODUCTION . Language models ( LMs ) play an important role across a range of natural language processing tasks , such as text summarization ( Rush et al. , 2015 ; Gehrmann et al. , 2018 ) , neural machine translation ( NMT ) ( Sutskever et al. , 2014 ; Cho et al. , 2014a ) , and image captioning ( Herdade et al. ,... | This paper introduces a global topic model into the Transformer to enrich longer-term dependencies beyond the fixed training segment, including contextual token/segment embedding (TE/SE) and Topic Attention (TA). However, some components seem to be unnecessary: The function of “+TA+SE” is very trivial by comparing “+To... | SP:684f590cdc9c5592b1f8d815d4b380186697d37d |
Multi-timescale Representation Learning in LSTM Language Models | 1 INTRODUCTION . Autoregressive language models are functions that estimate a probability distribution over the next word in a sequence from past words , p ( wt|wt−1 , . . . , w1 ) . This requires capturing statistical dependencies between words over short timescales , where syntactic information likely dominates ( Adi... | This work investigates representational power of LSTM to model natural language, in particular how well it models temporal dependencies within text. They define a notion of timescale of each LSTM unit and analitycally show that LSTM memory exhibits exponential decay, while natural language tends (based on prior work) t... | SP:351c576700ecf5c84f15b3a4baef958e72d099eb |
Multi-timescale Representation Learning in LSTM Language Models | 1 INTRODUCTION . Autoregressive language models are functions that estimate a probability distribution over the next word in a sequence from past words , p ( wt|wt−1 , . . . , w1 ) . This requires capturing statistical dependencies between words over short timescales , where syntactic information likely dominates ( Adi... | This paper points out the relationship between words in natural language usually follow the power law. Gated recurrent neural networks such as LSTMs excel in modelling natural language, however, the forgetting mechanism of LSTMs is ruled by the exponential decay. This work demonstrates a way to engineer the forgetting... | SP:351c576700ecf5c84f15b3a4baef958e72d099eb |
Consistency and Monotonicity Regularization for Neural Knowledge Tracing | 1 INTRODUCTION . In recent years , Artificial Intelligence in Education ( AIEd ) has gained much attention as one of the currently emerging fields in educational technology . In particular , the recent COVID-19 pandemic has transformed the setting of education from classroom learning to online learning . As a result , ... | This paper presents some enhancements for Knowledge Tracing (KT), in which predictions are made about the odds of a student answering a question correctly given a sequence of correct/incorrect responses to previous questions. The authors observe that the predictive model should obey certain 3 common sense constraints.... | SP:a9ff7a5c458d390f57d69569ab561dc077a46238 |
Consistency and Monotonicity Regularization for Neural Knowledge Tracing | 1 INTRODUCTION . In recent years , Artificial Intelligence in Education ( AIEd ) has gained much attention as one of the currently emerging fields in educational technology . In particular , the recent COVID-19 pandemic has transformed the setting of education from classroom learning to online learning . As a result , ... | Knowledge tracing is a longstanding task in educational data mining and has been tackled by various studies. This paper proposed that three data augmentation methods (along with different types of regularization losses) can be applied to boost the performance of existing deep neural network models for knowledge tracing... | SP:a9ff7a5c458d390f57d69569ab561dc077a46238 |
Bayesian neural network parameters provide insights into the earthquake rupture physics. | 1 INTRODUCTION . Because of the limited observational data and computational cost , geoscientists often rely on simple low-resolution simulations to study physical systems such as dynamic earthquake rupture , long-term tectonic process , etc . Such simplified models are indeed a powerful tool beside the observational d... | This paper proposes a Bayesian neural network for predicting if an earthquake will break a fault or not, overcoming 'small data problem' and predicting model uncertainty. The data is composed of 8 features and a binary output, and the samples are all coming from simulations. An analysis on the means and standard deviat... | SP:3a3df1703d2f59babf2fc1abd8b17975479fc8a5 |
Bayesian neural network parameters provide insights into the earthquake rupture physics. | 1 INTRODUCTION . Because of the limited observational data and computational cost , geoscientists often rely on simple low-resolution simulations to study physical systems such as dynamic earthquake rupture , long-term tectonic process , etc . Such simplified models are indeed a powerful tool beside the observational d... | In the present paper, the author intends to get further insights into the physics behind earthquake ruptures using a BNN to model simulated data from the literature. By using a BNN, the parameters of the model are not deterministic scalar values, but complete probability distributions. Studying the change of the distri... | SP:3a3df1703d2f59babf2fc1abd8b17975479fc8a5 |
Learning from Protein Structure with Geometric Vector Perceptrons | 1 INTRODUCTION . Many efforts in structural biology aim to predict , or derive insights from , the structure of a macromolecule ( such as a protein , RNA , or DNA ) , represented as a set of positions associated with atoms or groups of atoms in 3D Euclidean space . These problems can often be framed as functions mappin... | The challenge of predicting the structure of biological macro-molecules is widely relevant in many applications and difficult to address. This paper divides the types of approaches taken to address this challenge into those that use "geometric" information (i.e. positions of molecules in space), and those that utilize ... | SP:5042502317c1ae133d310b3ede02e7abde1a7507 |
Learning from Protein Structure with Geometric Vector Perceptrons | 1 INTRODUCTION . Many efforts in structural biology aim to predict , or derive insights from , the structure of a macromolecule ( such as a protein , RNA , or DNA ) , represented as a set of positions associated with atoms or groups of atoms in 3D Euclidean space . These problems can often be framed as functions mappin... | In this paper, the authors introduce a novel procedure to predict or acquire insights from the structure of a macromolecule (such as a protein, RNA, or DNA), represented as a set of positions associated with atoms or groups of atoms in 3D Euclidean space. Their approach, called GVP-GNN, can be applied to any problem wh... | SP:5042502317c1ae133d310b3ede02e7abde1a7507 |
On Representing (Anti)Symmetric Functions | 1 INTRODUCTION . Neural Networks ( NN ) , or more precisely , Multi-Layer Perceptrons ( MLP ) , are universal function approximators [ Pin99 ] in the sense that every ( say ) continuous function can be approximated arbitrarily well by a sufficiently large NN . The true power of NN though stems from the fact that they a... | This paper is about representing functions $\psi : (\mathbb{R}^d)^n \rightarrow \mathbb{R}$ that are symmetric or asymmetric with respect to the permutation group $S_n$. The aim is to consider neural networks giving only functions that symmetric or asymmetric, and to establish universality results. The motivation com... | SP:cd78fd328ffefb039b4f7629174f06f582a63920 |
On Representing (Anti)Symmetric Functions | 1 INTRODUCTION . Neural Networks ( NN ) , or more precisely , Multi-Layer Perceptrons ( MLP ) , are universal function approximators [ Pin99 ] in the sense that every ( say ) continuous function can be approximated arbitrarily well by a sufficiently large NN . The true power of NN though stems from the fact that they a... | In this paper the authors study the representability of symmetric or antisymmetric functions using neural networks. In particular, we say f: R^n -> R is symmetric/ antisymmetric if f(x)= f(pi(x)) for all pi in S_n or is of the form f(x)= sign(pi) *f(pi(x)) where sign(pi) is the sign of the permutation. Such functions w... | SP:cd78fd328ffefb039b4f7629174f06f582a63920 |
Robust early-learning: Hindering the memorization of noisy labels | The memorization effects of deep networks show that they will first memorize training data with clean labels and then those with noisy labels . The early stopping method therefore can be exploited for learning with noisy labels . However , the side effect brought by noisy labels will influence the memorization of clean... | This paper aims to exploit the early stopping method to solve the problem of learning with noisy labels. Specifically, this paper finds that only partial parameters (critical parameters) are important for fitting clean labels and generalize well; while the other parameters (non-critical parameters) tend to fit noisy la... | SP:28fa8254eddb2362bd1015b861efaf8c58edf29f |
Robust early-learning: Hindering the memorization of noisy labels | The memorization effects of deep networks show that they will first memorize training data with clean labels and then those with noisy labels . The early stopping method therefore can be exploited for learning with noisy labels . However , the side effect brought by noisy labels will influence the memorization of clean... | This paper tackles the problem of learning with noisy labels and proposes a novel method CDR which is inspired by the lottery ticket hypothesis. In particular, the proposed method categorizes the parameters into two parts, including critical parameters and non-critical parameters, and applies different update rules to ... | SP:28fa8254eddb2362bd1015b861efaf8c58edf29f |
Improving Abstractive Dialogue Summarization with Conversational Structure and Factual Knowledge | Recently , people have been paying more attention to the abstractive dialogue summarization task . Compared with news text , the information flows of the dialogue exchange between at least two interlocutors , which leads to the necessity of capturing long-distance cross-sentence relations . In addition , the generated ... | This paper proposes a knowledge graph enhanced network to improve abstractive dialog summarization with graphs constructed from the dialog structure and factual knowledge. The dialog graph is composed of utterances as nodes and 3 heuristic types of edges (such as utterances of the same speaker, adjacent utterances). Th... | SP:63621148403a12bfbf0dd14179ad3933d6ebe50d |
Improving Abstractive Dialogue Summarization with Conversational Structure and Factual Knowledge | Recently , people have been paying more attention to the abstractive dialogue summarization task . Compared with news text , the information flows of the dialogue exchange between at least two interlocutors , which leads to the necessity of capturing long-distance cross-sentence relations . In addition , the generated ... | This paper proposes to improve dialogue summarization by encoding the text with a sequential encoder (for token-level contextualization) and a graph encoder (for long-distance and semantic contextualization). A KG is built and considered to be a surrogate for "factual knowledge". A dual-copy mechanism is used while dec... | SP:63621148403a12bfbf0dd14179ad3933d6ebe50d |
NOSE Augment: Fast and Effective Data Augmentation Without Searching | 1 INTRODUCTION . Data is an essential and dominant factor for learning AI models , especially in deep learning era where deep neural networks normally require large data volume for training . Data augmentation techniques artificially create new samples to increase the diversity of training data and in turn the generali... | This paper aims to provide an effective augmentation strategy without the need for a separate search. The resulting method is called NOSE Augment, which is presented as a substitute for the previous AutoAugment type methods (e.g. Fast AutoAugment, Population Based Augmentation, RandAugment, Adversarial AutoAugment etc.... | SP:95a437dfc5ea49319e4f23691decffac1d4a74b3 |
NOSE Augment: Fast and Effective Data Augmentation Without Searching | 1 INTRODUCTION . Data is an essential and dominant factor for learning AI models , especially in deep learning era where deep neural networks normally require large data volume for training . Data augmentation techniques artificially create new samples to increase the diversity of training data and in turn the generali... | The authors propose a method for learning an augmentation pipeline for image recognition. As opposed to recent existing approaches such as AutoAugment or RandAugment, the authors do not seek for the augmentation pipeline iteratively. Instead they use a stochastic approach, where augmenters are split to three categories... | SP:95a437dfc5ea49319e4f23691decffac1d4a74b3 |
Learning from others' mistakes: Avoiding dataset biases without modeling them | 1 INTRODUCTION . The natural language processing community has made tremendous progress in using pre-trained language models to improve predictive accuracy ( Devlin et al. , 2019 ; Raffel et al. , 2019 ) . Models have now surpassed human performance on language understanding benchmarks such as SuperGLUE ( Wang et al. ,... | This paper focuses on the known problem that current NLP models tend to solve tasks by exploiting superficial properties of the training data that do not generalize. For example, in the NLI task, models learn that negation words are indicative of the label "contradiction" and high word overlap is indicative of the labe... | SP:aecd57c5b2337667d1619d0da05b1103c8ba07cf |
Learning from others' mistakes: Avoiding dataset biases without modeling them | 1 INTRODUCTION . The natural language processing community has made tremendous progress in using pre-trained language models to improve predictive accuracy ( Devlin et al. , 2019 ; Raffel et al. , 2019 ) . Models have now surpassed human performance on language understanding benchmarks such as SuperGLUE ( Wang et al. ,... | This paper proposes a method for training model that are robust to spurious correlations, building upon prior work that uses product-of-experts and a model explicitly trained on a dataset bias (e.g., a hypothesis-only model). Instead of using a model explicitly trained to learn the dataset bias, the authors use a “weak... | SP:aecd57c5b2337667d1619d0da05b1103c8ba07cf |
Hyperbolic Neural Networks++ | 1 INTRODUCTION . Shifting the arithmetic stage of a neural network to a non-Euclidean geometry such as a hyperbolic space is a promising way to find more suitable geometric structures for representing or processing data . Owing to its exponential growth in volume with respect to its radius ( Krioukov et al. , 2009 ; 20... | Hyperbolic Neural Networks++ extends the existing work of applying hyperbolic manifolds to neural networks. It proposes new ways to reparametrize hyperbolic multinomial logistic regression (MLR) layers to reduce the number of parameters, to generalise fully connected layers as well as split and concat operations to be... | SP:e7c555dbd995aa8446a7a8ac705aa9bfced8ac9a |
Hyperbolic Neural Networks++ | 1 INTRODUCTION . Shifting the arithmetic stage of a neural network to a non-Euclidean geometry such as a hyperbolic space is a promising way to find more suitable geometric structures for representing or processing data . Owing to its exponential growth in volume with respect to its radius ( Krioukov et al. , 2009 ; 20... | The paper provides a reformulation of the fundamental operations in Euclidean space that are used in neural networks for the Poincaré ball model of hyperbolic space (and thus hyperbolic space generally). The paper’s reformulation differs from previous reformulations (Ganea et al. 2018) in several ways. For multinomial ... | SP:e7c555dbd995aa8446a7a8ac705aa9bfced8ac9a |
Meta-learning Transferable Representations with a Single Target Domain | 1 INTRODUCTION . Transfer learning—transferring knowledge learned from a large-scale source dataset to a small target dataset—is an important paradigm in machine learning ( Yosinski et al. , 2014 ) with wide applications in vision ( Donahue et al. , 2014 ) and natural language processing ( NLP ) ( Howard & Ruder , 2018... | The paper investigates failure cases for transfer learning (fine-tuning and joint training), specifically in the context where training on the source data may highlight features that are irrelevant for the target data. This is done through semi-synthetic data. Based on the insights, the authors present an approach call... | SP:42c3eb2cad23ed513b09f0810e3d7dd4e7bb2532 |
Meta-learning Transferable Representations with a Single Target Domain | 1 INTRODUCTION . Transfer learning—transferring knowledge learned from a large-scale source dataset to a small target dataset—is an important paradigm in machine learning ( Yosinski et al. , 2014 ) with wide applications in vision ( Donahue et al. , 2014 ) and natural language processing ( NLP ) ( Howard & Ruder , 2018... | This paper first investigates when and why fine-tuning and joint training are not the best methods for transfer learning. The authors generate a toy dataset, in which the source-specific and transferable features are thus clearly distinguishable, for the transfer learning tasks. As both fine-tuning and joint training a... | SP:42c3eb2cad23ed513b09f0810e3d7dd4e7bb2532 |
A Good Image Generator Is What You Need for High-Resolution Video Synthesis | 1 INTRODUCTION . Video synthesis seeks to generate a sequence of moving pictures from noise . While its closely related counterpart—image synthesis—has seen substantial advances in recent years , allowing for synthesizing at high resolutions ( Karras et al. , 2017 ) , rendering images often indistinguishable from real ... | This paper proposes a method to disentangle content and motion from videos for high-resolution video synthesis. The proposed method consists of a motion generator, pre-trained generator, image discriminator, and video discriminator. The motion generator predicts the latent motion trajectory z, which is residually updat... | SP:626c65a0fe391afe1b3a8769de16e9f1ccded977 |
A Good Image Generator Is What You Need for High-Resolution Video Synthesis | 1 INTRODUCTION . Video synthesis seeks to generate a sequence of moving pictures from noise . While its closely related counterpart—image synthesis—has seen substantial advances in recent years , allowing for synthesizing at high resolutions ( Karras et al. , 2017 ) , rendering images often indistinguishable from real ... | This paper addresses the problem of video synthesis --- generating diverse, realistic videos. This paper's core idea is to leverage a fixed, pre-trained GAN model for image synthesis and train a motion generator to produce a sequence of latent vectors to generate image sequences (using the pretrained GAN and the genera... | SP:626c65a0fe391afe1b3a8769de16e9f1ccded977 |
Learning continuous-time PDEs from sparse data with graph neural networks | 1 INTRODUCTION . We consider continuous dynamical systems with a state u ( x , t ) ∈ R that evolves over time t ∈ R+ and spatial locations x ∈ Ω ⊂ RD of a bounded domain Ω . We assume the system is governed by an unknown partial differential equation ( PDE ) u̇ ( x , t ) : = du ( x , t ) dt = F ( x , u , ∇xu , ∇2xu , .... | The paper proposes to use graph-based networks for evaluations of PDEs with continuous time formulations. In contrast to existing works on continuous time ODE formulations with graph structures, the proposed networks incorporate relative spatial information in order for the network to evaluate spatial derivatives in ad... | SP:c395c4430a7169941643a29482096a5d69b7ae50 |
Learning continuous-time PDEs from sparse data with graph neural networks | 1 INTRODUCTION . We consider continuous dynamical systems with a state u ( x , t ) ∈ R that evolves over time t ∈ R+ and spatial locations x ∈ Ω ⊂ RD of a bounded domain Ω . We assume the system is governed by an unknown partial differential equation ( PDE ) u̇ ( x , t ) : = du ( x , t ) dt = F ( x , u , ∇xu , ∇2xu , .... | This submission proposes extensions of PDE-net that relax some constraints that could help extend the range of applications of this approach. First, rather than fixing a spatial discretization in the form of a grid, the authors use a Delaunay triangulation to represent the domain. The updates to the nodes of this trian... | SP:c395c4430a7169941643a29482096a5d69b7ae50 |
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks | 1 INTRODUCTION . Deep Learning networks have tremendously improved state-of-the-art performance for many tasks such as object detection , classification and natural language processing ( Krizhevsky et al. , 2012 ; Hinton et al. , 2012 ; Deng & Liu , 2018 ) . However , such architectures are extremely energyintensive ( ... | This paper proposes an encoding method based on the Discrete Cosine Transform (DCT) for Spiking Neural Network (SNN). The key idea is to decompose an image into different frequency components and feed them to the SNN sequentially. Compared to the Poisson coding method used in most SNN studies, the proposed encoding met... | SP:7ded50d27f7af0a20575038f245fec224285d066 |
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks | 1 INTRODUCTION . Deep Learning networks have tremendously improved state-of-the-art performance for many tasks such as object detection , classification and natural language processing ( Krizhevsky et al. , 2012 ; Hinton et al. , 2012 ; Deng & Liu , 2018 ) . However , such architectures are extremely energyintensive ( ... | The scheme proposed breaks down the information in a block of an image into orthogonal basis functions (DCT is used) to make a progressively better reconstruction of the original image block with the addition of more basis functions used (like an nth order Taylor expansion). The increasing spatial frequency components... | SP:7ded50d27f7af0a20575038f245fec224285d066 |
Individually Fair Gradient Boosting | 1 INTRODUCTION . In light of the ubiquity of machine learning ( ML ) methods in high-stakes decision-making and support roles , there is concern about ML models reproducing or even exacerbating the historical biases against certain groups of users . These concerns are valid : there are recent incidents in which algorit... | The authors presented in the submission a thorough study on enforcing the aggregated individual fairness with non-differentiable ML models. The proposed method generates individually fair and robust ML models in a minimax fashion among all possible samples that are close to the true distribution w.r.t. a given fair met... | SP:0a53cecba6b2eda47d14aacdd8e9873c8c1cce5b |
Individually Fair Gradient Boosting | 1 INTRODUCTION . In light of the ubiquity of machine learning ( ML ) methods in high-stakes decision-making and support roles , there is concern about ML models reproducing or even exacerbating the historical biases against certain groups of users . These concerns are valid : there are recent incidents in which algorit... | This paper proposes a non-smooth method to enforce individual fairness in gradient boosting. To deal with the non-smoothness of the model, it restricts the optimal transport distance to that defined on an augmented training support set and thus reduces the search of a worst-case distribution to solving an LP problem, w... | SP:0a53cecba6b2eda47d14aacdd8e9873c8c1cce5b |
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design | 1 INTRODUCTION . Computational protein design is the conceptual inverse of the protein structure prediction problem , and aims to infer an amino acid sequence that will fold into a given 3D structure . Designing protein sequences that will fold into a desired structure has a broad range of applications , from therapeut... | This paper tackle the challenge of designing protein sequences that are consistent with a given 3D fold. To address this challenge, the authors propose a transformer-based generative framework that designs protein sequences conditioned on a given fold. There are two central contributions - the first is a novel fold rep... | SP:ee58edd03a89f8fa07ba73d3b36b32ad551970da |
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design | 1 INTRODUCTION . Computational protein design is the conceptual inverse of the protein structure prediction problem , and aims to infer an amino acid sequence that will fold into a given 3D structure . Designing protein sequences that will fold into a desired structure has a broad range of applications , from therapeut... | This manuscript presents a method for generating protein sequences conditioned on protein structures. The core idea is to represented protein structures by their secondary structures in 3D space. This voxel grid is then encoded into a vector representation and decoded to a distribution over sequences. The authors propo... | SP:ee58edd03a89f8fa07ba73d3b36b32ad551970da |
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing | 1 INTRODUCTION . Ever since Szegedy et al . ( 2016 ) introduced label smoothing as a way to regularize the classification ( or output ) layer of a deep neural network , it has been used across a wide range of tasks from image classification ( Szegedy et al. , 2016 ) and machine translation ( Vaswani et al. , 2017 ) to ... | This paper theoretically analyzes "label smoothing” (LS) with PAC-Bayesian bound and motivated from their analysis, proposes a new method: LORAS. In their theoretical analysis, they identify that the generalization error depends on the smoothing distribution. So they propose to learn the smoothing distribution in LOR... | SP:f19f24d32d2198e2512cee9f90386a5175255624 |
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing | 1 INTRODUCTION . Ever since Szegedy et al . ( 2016 ) introduced label smoothing as a way to regularize the classification ( or output ) layer of a deep neural network , it has been used across a wide range of tasks from image classification ( Szegedy et al. , 2016 ) and machine translation ( Vaswani et al. , 2017 ) to ... | The paper proposes a label-smoothing method upon the low-rank assumption of the output dimension, especially when the output dimension is large. The contribution of this work is two folds: first, highlighted the importance of informative label smoothing through better bound, and second, proposed one label smoothing wit... | SP:f19f24d32d2198e2512cee9f90386a5175255624 |
Learning Invariant Representations for Reinforcement Learning without Reconstruction | We study how representation learning can accelerate reinforcement learning from rich observations , such as images , without relying either on domain knowledge or pixel-reconstruction . Our goal is to learn representations that provide for effective downstream control and invariance to task-irrelevant details . Bisimul... | The paper presents a new method for embedding visual images into a state space suitable for effective control by an actor-critic style RL algorithm. They show how a previously explored idea of using a bisimulation between state abstractions and reward sequences to group states that are similar from a decision theoretic... | SP:ee9150a3bbdc0fa36a2a1876c4de2c9002270a06 |
Learning Invariant Representations for Reinforcement Learning without Reconstruction | We study how representation learning can accelerate reinforcement learning from rich observations , such as images , without relying either on domain knowledge or pixel-reconstruction . Our goal is to learn representations that provide for effective downstream control and invariance to task-irrelevant details . Bisimul... | The paper focuses on how learning state-representations that encode information relevant to the task can improve reinforcement learning from pixels. Often, observations in an MDP can contain information that are irrelevant (“distractors”) to the task at hand and can likely “distract” the downstream RL algorithm used. U... | SP:ee9150a3bbdc0fa36a2a1876c4de2c9002270a06 |
Can Students Outperform Teachers in Knowledge Distillation based Model Compression? | 1 INTRODUCTION . Deep neural networks ( DNNs ) have achieved remarkable performances in various domains , but they require large amounts of computation and memory . This seriously limits their deployment with limited resources or a strict latency requirement . One solution to this problem is knowledge distillation whic... | The paper studies knowledge distillation. In particular, it tries to disentangle the effect of student model capacity and distillation dataset on the performance of the student. The paper goes on to present KD+, a knowledge distillation approach that goes beyond in-distribution data. Experiments on multiple image recog... | SP:f14e55698af04d3b1990c9cc6f86951b56276742 |
Can Students Outperform Teachers in Knowledge Distillation based Model Compression? | 1 INTRODUCTION . Deep neural networks ( DNNs ) have achieved remarkable performances in various domains , but they require large amounts of computation and memory . This seriously limits their deployment with limited resources or a strict latency requirement . One solution to this problem is knowledge distillation whic... | This paper presents an argument that model capacity differences are not necessarily the root reason for the performance gap between the student and the teacher, and the distillation data matters when the student capacity is greater than a threshold. Based on this, the authors develop KD+ to reduce the performance gap b... | SP:f14e55698af04d3b1990c9cc6f86951b56276742 |
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples | 1 INTRODUCTION Deep neural network ( DNN ) classifiers have become indispensable tools for addressing practically relevant problems , such as autonomous driving ( Tian et al. , 2018 ) , natural language processing ( Young et al. , 2018 ) and health care predictions ( Esteva et al. , 2019 ) . While a DNN provides substa... | This paper introduces an interesting property of adversarial examples, which is called conferrability and can reflect the abilities whether an instance can exclusively transfer with a target label from a source model to its surrogates. A new method is proposed to generate conferrable adversarial examples. Experimental ... | SP:68a116045efaad7a809e686eab1b5a8b1f665d99 |
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples | 1 INTRODUCTION Deep neural network ( DNN ) classifiers have become indispensable tools for addressing practically relevant problems , such as autonomous driving ( Tian et al. , 2018 ) , natural language processing ( Young et al. , 2018 ) and health care predictions ( Esteva et al. , 2019 ) . While a DNN provides substa... | This paper studies fingerprinting a neural network model by using adversarial example techniques. The idea itself is interesting enough, the this work presents a neat development toward solving this problem. An important issue with this problem is to distinguish a reference model from a stolen model. Thus a desire prop... | SP:68a116045efaad7a809e686eab1b5a8b1f665d99 |
Factor Normalization for Deep Neural Network Models | 1 INTRODUCTION . In recent decades , the progress of deep learning , together with advances in GPU devices , has led to a growing popularity of deep neural network ( DNN ) models in both academia and industry . DNN models have been widely used in various fields , such as image classification ( Simonyan & Zisserman , 20... | In this paper, a learning method that accelerates the training of DNN is proposed. Given an input X, the proposed method decomposes X as X = BZ + E where BZ is a low-rank approximation of X and E is the residual term. E is used as an input of DNN and Z is used as an additional feature of the input of the last layer. Ex... | SP:974f46bba59746cb294a5107730c8bc0d27cca85 |
Factor Normalization for Deep Neural Network Models | 1 INTRODUCTION . In recent decades , the progress of deep learning , together with advances in GPU devices , has led to a growing popularity of deep neural network ( DNN ) models in both academia and industry . DNN models have been widely used in various fields , such as image classification ( Simonyan & Zisserman , 20... | The paper describes a training scheme based on decomposing input features into two parts which have different training dynamics: a low rank "factor feature" computed using PCA on the raw features, and a high rank "residual". The former is processed by a very shallow network, while the latter passes through the full n... | SP:974f46bba59746cb294a5107730c8bc0d27cca85 |
Revisiting Loss Modelling for Unstructured Pruning | 1 INTRODUCTION . Neural networks are getting bigger , requiring more and more computational resources not only for training , but also when used for inference . However , resources are sometimes limited , especially on mobile devices and low-power chips . In unstructured pruning , the goal is to remove some parameters ... | - This paper conducted a detailed study on how does the loss modeling affects the final performance of the pruned model. The authors first provided a unified view of various pruning algorithms (e.g., Magnitude Pruning, SNIP, OBD, and OBS), which can be categorized into three classes: weight magnitude, linear and quadra... | SP:69a60eb62ae3b7e2bb036b1434554397024a8d47 |
Revisiting Loss Modelling for Unstructured Pruning | 1 INTRODUCTION . Neural networks are getting bigger , requiring more and more computational resources not only for training , but also when used for inference . However , resources are sometimes limited , especially on mobile devices and low-power chips . In unstructured pruning , the goal is to remove some parameters ... | The authors study the use of loss-modeling to maintain model quality when inducing unstructured sparsity in deep neural networks. They study a range of different approximations and modifications that can help improve the quality of the approximation (taking local steps, avoid large changes in weight magnitude, avoiding... | SP:69a60eb62ae3b7e2bb036b1434554397024a8d47 |
DEMI: Discriminative Estimator of Mutual Information | Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data . Recent progress has leveraged neural networks to optimize variational lower bounds on mutual information . Although showing promise for this difficult problem , the variational me... | This paper proposed a discriminative estimator for mutual information, to alleviate the shortcomings of the existing estimators such as MINE and SMILE. A classifier was built to decide whether the sample is drawn from the joint distribution or the independent one (product of marginals). Theoretical justification and ex... | SP:810aaef2f0ad54bbd1a57053bae7860324e17602 |
DEMI: Discriminative Estimator of Mutual Information | Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data . Recent progress has leveraged neural networks to optimize variational lower bounds on mutual information . Although showing promise for this difficult problem , the variational me... | This paper proposes DEMI, a discriminative approach to estimate mutual information (MI). The main idea is that, instead of learning (generative) distributions of joint and marginals, learning a single likelihood ratio that is discriminative and hence more tractable: a posterior $p(z | x, y)$ trying to distinguish betwe... | SP:810aaef2f0ad54bbd1a57053bae7860324e17602 |
Non-robust Features through the Lens of Universal Perturbations | 1 INTRODUCTION . Modern deep neural networks perform extremely well across many prediction tasks , but they largely remain vulnerable to adversarial examples ( Szegedy et al. , 2014 ) . Models ’ brittleness to these small , imperceptible perturbations highlights one alarming way in which models deviate from humans . Re... | Prior works generally thought non-robust features, which are vulnerable to small perturbations, are not semantically meaningful but are useful for generalization. This work challenges these traditional beliefs by pointing out that non-robust features can also be human-perception aligned and be less useful for generaliz... | SP:3798e47ac56ab60bb2a913429c72ed2dff66531a |
Non-robust Features through the Lens of Universal Perturbations | 1 INTRODUCTION . Modern deep neural networks perform extremely well across many prediction tasks , but they largely remain vulnerable to adversarial examples ( Szegedy et al. , 2014 ) . Models ’ brittleness to these small , imperceptible perturbations highlights one alarming way in which models deviate from humans . Re... | This paper studies the link between non-robust features and universal adversarial perturbations. This paper shows that universal perturbation leverage non-robust features in data in a different way than standard adversarial attacks. Experiments are based on a universal version of projected gradient descent (PGD). The f... | SP:3798e47ac56ab60bb2a913429c72ed2dff66531a |
Ablation Path Saliency | 1 Introduction The basic idea of saliency or attribution is to provide something from which a human can judge how a classifier arrived at its decision of the prediction it gives for a certain input . It is difficult to give a more mathematical definition , but various properties that such a method should fulfill have b... | The paper describes the black box approach to saliency prediction using ablation paths gradually replacing the parts of the image of one class with the image of another one. The idea has certain novelty, but the reviewer cannot see that it is backed by the evidence. The following comments are describing the reasons for... | SP:e5f3f418dfcf0d45c37cfc237aee269a3b9bfad3 |
Ablation Path Saliency | 1 Introduction The basic idea of saliency or attribution is to provide something from which a human can judge how a classifier arrived at its decision of the prediction it gives for a certain input . It is difficult to give a more mathematical definition , but various properties that such a method should fulfill have b... | Saliency problem for black-box classification is the main focus in this paper, which means to find out the part of the image that is most relevant for the current model decision. Authors propose to find an optimal ablation path between two images to get such saliency maps. The finding in this paper suggest a new view ... | SP:e5f3f418dfcf0d45c37cfc237aee269a3b9bfad3 |
A new framework for tensor PCA based on trace invariants | 1 INTRODUCTION . Powerful computers and acquisition devices have made it possible to capture and store real-world multidimensional data . For practical applications ( Kolda & Bader ( 2009 ) ) , analyzing and organizing these high dimensional arrays ( formally called tensors ) lead to the well known curse of dimensional... | This paper studies the detection and recovery problem in spiked tensor models in the form T = \beta v0^\otimes k + Z, where v0 is the underlying spike signal and Z is a Gaussian noise. The authors claim that they propose a new framework to solve the problem, by looking at the trace invariants of tensors. The authors p... | SP:a8d2b848db3e06293f0fd3c0a3a3a45e02dad92a |
A new framework for tensor PCA based on trace invariants | 1 INTRODUCTION . Powerful computers and acquisition devices have made it possible to capture and store real-world multidimensional data . For practical applications ( Kolda & Bader ( 2009 ) ) , analyzing and organizing these high dimensional arrays ( formally called tensors ) lead to the well known curse of dimensional... | The paper presents a pair of interesting algorithms using trace invariants to detect the signal in the signal-plus-noise tensor PCA framework. The algorithms function by considering cutting an edge in the graph representation of the trace invariant, yielding a matrix whose leading eigenvector provides a (up to a rotat... | SP:a8d2b848db3e06293f0fd3c0a3a3a45e02dad92a |
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels | 1 INTRODUCTION . Sample-efficient deep reinforcement learning ( RL ) algorithms capable of directly training from image pixels would open up many real-world applications in control and robotics . However , simultaneously training a convolutional encoder alongside a policy network is challenging when given limited envir... | This paper investigates data augmentation in the context of RL and proposes a novel augmentation algorithm to enabling robust learning directly from pixels without the need for auxiliary losses or pre-training. The authors propose to average both the Q function and its target over multiple image transformations. The ex... | SP:f68cb310366c55ef0bb74f3ec61cf95eb62f8f9c |
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels | 1 INTRODUCTION . Sample-efficient deep reinforcement learning ( RL ) algorithms capable of directly training from image pixels would open up many real-world applications in control and robotics . However , simultaneously training a convolutional encoder alongside a policy network is challenging when given limited envir... | To enable robust policy learning with image observations, the paper proposes a simple data augmentation technique that can be used with existing model-free reinforcement learning algorithms. It defines a notion of optimality invariant state transformation which preserves the Q function. An example of such transformatio... | SP:f68cb310366c55ef0bb74f3ec61cf95eb62f8f9c |
Bridging Graph Network to Lifelong Learning with Feature Interaction | 1 Introduction . Graph neural networks ( GNN ) have received increasing attention and proved to be useful for many tasks with graph-structured data , such as citation , social , and protein networks [ 1 ] . However , graph data is usually formed in a streaming fashion and real-world data are continuously evolving over ... | This paper aims to bridge GNNs with life-long learning so that the catastrophic forgetting problem in graph-structured tasks is alleviated. Specifically, the major contribution seems to be transforming the original graph into a feature graph so that the node classification problem is transferred into a graph classifica... | SP:c86b5fd03baefacd2e70d21e5b41faf8b97bb29b |
Bridging Graph Network to Lifelong Learning with Feature Interaction | 1 Introduction . Graph neural networks ( GNN ) have received increasing attention and proved to be useful for many tasks with graph-structured data , such as citation , social , and protein networks [ 1 ] . However , graph data is usually formed in a streaming fashion and real-world data are continuously evolving over ... | This paper aims to solve the problem of lifelong graph learning. Thus far, the topic about graph learning and lifelong learning is still underexplored. This paper proposes a new graph topology based on feature interaction, which takes the features as nodes and turns the nodes into graphs, and thus formulates a regular ... | SP:c86b5fd03baefacd2e70d21e5b41faf8b97bb29b |
Simple Spectral Graph Convolution | 1 INTRODUCTION . In the past decade , deep learning has become mainstream in computer vision and machine learning . Although deep learning has been applied for extraction of features on the Euclidean lattice ( Euclidean grid-structured data ) with great success , the data in many practical scenarios lies on non-Euclide... | The paper proposes a spectral-based graph convolution layer, called Simple Spectral Graph Convolution (S$^2$GC), which is based on the Markov Diffusion Kernel (MDK). The authors show that S$^2$GC is capable of aggregating k-hop neighbourhood information without oversmoothing. The paper provides a spectral analysis on S... | SP:79f20a38e4adada1f0757f8a01cd3d1b840b7b2e |
Simple Spectral Graph Convolution | 1 INTRODUCTION . In the past decade , deep learning has become mainstream in computer vision and machine learning . Although deep learning has been applied for extraction of features on the Euclidean lattice ( Euclidean grid-structured data ) with great success , the data in many practical scenarios lies on non-Euclide... | One of the most important component of GCN is coming with suitable graph filters and crucial towards designing better GCNs. In this regard, authors proposed Simple Spectral Graph Convolution with Markov diffusion kernel as a graph filter which combines strengths of both spatial and spectral methods. The paper is writte... | SP:79f20a38e4adada1f0757f8a01cd3d1b840b7b2e |
Robust Curriculum Learning: from clean label detection to noisy label self-correction | 1 INTRODUCTION . The expressive power and high capacity of deep neural networks ( DNNs ) result in accurate modeling and promising generalization if provided with sufficient data and clean ( correct ) labels . However , recent studies show that the training process is fragile and can easily overfit on noisy labels ( Zh... | This paper proposes a curriculum learning method to handle noisily labeled data. The idea is to introduce a consistency measure instead of directly apply a loss function for the typical supervised learning, where the specific consistency is measure for both temporal dimension along neighboring steps and spatial dimensi... | SP:c7e7a9e3047b95af8862d79681b3c47ada2642b2 |
Robust Curriculum Learning: from clean label detection to noisy label self-correction | 1 INTRODUCTION . The expressive power and high capacity of deep neural networks ( DNNs ) result in accurate modeling and promising generalization if provided with sufficient data and clean ( correct ) labels . However , recent studies show that the training process is fragile and can easily overfit on noisy labels ( Zh... | This article is concerned with the problem of training models under noisy data. The authors first adopt the loss and output consistency for data selection. EMA method is used for smoothing to obtain more accurate clean label detection. Meanwhile, through the introduction of temperature hyperparameters, the model gradua... | SP:c7e7a9e3047b95af8862d79681b3c47ada2642b2 |
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling | 1 INTRODUCTION . Distributed or federated machine learning , where the data is distributed across multiple workers , has become an increasingly important learning paradigm both due to growing sizes of datasets , as well as privacy and security concerns . In such a setting , the workers collaborate to train a single mod... | The paper studies Byzantine robustness in the context of distributed learning from heterogeneous datasets. This problem has been widely studied previously, but under the additional assumption that the data of the good workers is i.i.d.. The authors give examples of situations and poisoning attacks with which current de... | SP:72f25cb455c7d0e0a2ce818d3baab4a916376dd3 |
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