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Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms | We benchmark commonly used multi-agent deep reinforcement learning ( MARL ) algorithms on a variety of cooperative multi-agent games . While there has been significant innovation in MARL algorithms , algorithms tend to be tested and tuned on a single domain and their average performance across multiple domains is less ... | The paper aims to benchmark a suite of Multi-Agent Deep Reinforcement Learning algorithms across different environments in the cooperative multi-agent setting. The paper compares standard algorithms alongside extensions of well-known policy gradient algorithms to the multi-agent setting, i.e. PPO (MAPPO), SAC (MASAC) a... | SP:2e305b4762d57663e3c96ae164a9cd385dfe9549 |
A Multi-Modal and Multitask Benchmark in the Clinical Domain | Healthcare represents one of the most promising application areas for machine learning algorithms , including modern methods based on deep learning . Modern deep learning algorithms perform best on large datasets and on unstructured modalities such as text or image data ; advances in deep learning have often been drive... | This paper defines a new task for clinical data by combing multi-modal and multi-task settings into one task. It collects a dataset called M3 as the benchmark for the multi-modal and multi-task benchmark in the clinical domain. The dataset has 6 prediction tasks, i.e., in-hospital mortality, decompensation, length of s... | SP:834d63ae7b52ef284c72e35188bb5722141fcd5d |
A Multi-Modal and Multitask Benchmark in the Clinical Domain | Healthcare represents one of the most promising application areas for machine learning algorithms , including modern methods based on deep learning . Modern deep learning algorithms perform best on large datasets and on unstructured modalities such as text or image data ; advances in deep learning have often been drive... | This paper discusses the Multi-Modal Multi-Task MIMIC-III (M3) dataset and benchmark, which extends previous efforts in this space to provide a benchmark on the MIMIC-III dataset. In particular, this work considers the inclusion of multiple modalities, including time series, clinical notes, ECG waveforms, and tabular i... | SP:834d63ae7b52ef284c72e35188bb5722141fcd5d |
Efficient Empowerment Estimation for Unsupervised Stabilization | 1 INTRODUCTION . Intrinsic motivation allows artificial and biological agents to acquire useful behaviours without external knowledge ( Barto et al . ( 2004 ) ; Chentanez et al . ( 2005 ) ; Schmidhuber ( 2010 ) ; Barto ( 2013 ) ; Oudeyer et al . ( 2016 ) ) . In the framework of reinforcement learning ( RL ) , this exte... | The paper proposes an new algorithm to simultaneously estimate and maximise empowerment for achieving unsupervised stabilization. The method relies on the formulation of a dynamic system as a linear Gaussian channel. In this formulation, empowerment can be efficiently estimated by solving a line search problem. The aut... | SP:dc768f825220140d0fa00fe2f63673803973c19c |
Efficient Empowerment Estimation for Unsupervised Stabilization | 1 INTRODUCTION . Intrinsic motivation allows artificial and biological agents to acquire useful behaviours without external knowledge ( Barto et al . ( 2004 ) ; Chentanez et al . ( 2005 ) ; Schmidhuber ( 2010 ) ; Barto ( 2013 ) ; Oudeyer et al . ( 2016 ) ) . In the framework of reinforcement learning ( RL ) , this exte... | The paper studies reward-free reinforcement learning (RL) methods based on empowerment. The authors propose a technique for empowerment estimation under the assumption that a state after H timesteps can be factorized as a product of the current action and a matrix G(s) that depends on the current state. In contrast to ... | SP:dc768f825220140d0fa00fe2f63673803973c19c |
Pretrain Knowledge-Aware Language Models | 1 INTRODUCTION . The strong effectiveness and rich generalization ability of pretrained language models ( PLMs ) ( 1 ; 2 ; 3 ; 4 ; 5 ) have raised many questions about what is captured in transformer networks and why . Recent explorations found the pretrained language models may “ rediscover ” the linguistic pipeline a... | This paper presents a knowledge-aware language model pretraining method without changing model architecture. Specifically, they add entity prediction task along with language modeling task to make the model aware of knowledge. Experiments show improved results on the LAMA knowledge probing task compared to GPT-2 models... | SP:1f15c0a7d7bdf113c303a89650afb22f9d56abad |
Pretrain Knowledge-Aware Language Models | 1 INTRODUCTION . The strong effectiveness and rich generalization ability of pretrained language models ( PLMs ) ( 1 ; 2 ; 3 ; 4 ; 5 ) have raised many questions about what is captured in transformer networks and why . Recent explorations found the pretrained language models may “ rediscover ” the linguistic pipeline a... | This paper proposes KALM - a knowledge-aware language model that incorporates entity information. Specifically, the method involves using a dictionary lookup to match n-grams to entities from a database. Then, each token is embedded into two vectors — one using the surface form, and one using the entity ID it is matche... | SP:1f15c0a7d7bdf113c303a89650afb22f9d56abad |
Nonseparable Symplectic Neural Networks | 1 INTRODUCTION . A Hamiltonian dynamic system refers to a formalism for modeling a physical system exhibiting some specific form of energy conservation during its temporal evolution . A typical example is a pendulum whose total energy ( referred to as the system ’ s Hamiltonian ) is conserved as a temporally invariant ... | The paper extends the symplectic family of network architectures towards modeling nonseparable Hamiltonian dynamic systems. More specifically, the paper implements a symplectic integration schema (from Tao (2016)) for solving arbitrary nonseparable (and separable) Hamiltonian systems within a symplectic neural network... | SP:b2243f53f080e0babbc6061de7e2c2059a8d0cf0 |
Nonseparable Symplectic Neural Networks | 1 INTRODUCTION . A Hamiltonian dynamic system refers to a formalism for modeling a physical system exhibiting some specific form of energy conservation during its temporal evolution . A typical example is a pendulum whose total energy ( referred to as the system ’ s Hamiltonian ) is conserved as a temporally invariant ... | This paper describes a deep learning approach for predicting Hamiltonian systems. The original paper enforces conservation in the loss function. Several of the follow-up papers embed a symplectic integrator instead, but these couldn't handle non-separable systems. This paper can both handle non-separable systems and us... | SP:b2243f53f080e0babbc6061de7e2c2059a8d0cf0 |
Cooperating RPN's Improve Few-Shot Object Detection | 1 INTRODUCTION . Achieving accurate few-shot object detection is difficult , because one must rely on a classifier building a useful model of variation in appearance with very few examples . This paper identifies an important effect that causes existing detectors to have weaker than necessary performance in the few-sho... | In this paper, the authors present a interesting, novel idea of promoting the diversity and cooperation among multiple RPNs for the problem of few-shot detection. They first identify a critical problem in few-shot detection, which is that existing RPNs can miss objects of novel classes and that their proposed boxes mak... | SP:c171fccf8cd846266842b5fc4896477903b9bdb5 |
Cooperating RPN's Improve Few-Shot Object Detection | 1 INTRODUCTION . Achieving accurate few-shot object detection is difficult , because one must rely on a classifier building a useful model of variation in appearance with very few examples . This paper identifies an important effect that causes existing detectors to have weaker than necessary performance in the few-sho... | The authors propose a few-show object detection architecture, which improves the 1st stage of two-stage detectors (R-CNN in particular). In a few shot setting, the existing approaches the region proposal generator may ignore some novel out-of-distribution classes as they have not been included at training time. The pro... | SP:c171fccf8cd846266842b5fc4896477903b9bdb5 |
The Impact of the Mini-batch Size on the Dynamics of SGD: Variance and Beyond | 1 INTRODUCTION . Deep learning models have achieved great success in a variety of tasks including natural language processing , computer vision , and reinforcement learning ( Goodfellow et al. , 2016 ) . Despite their practical success , there are only limited studies of the theoretical properties of deep learning ; se... | This paper studies the variance of stochastic gradient in SGD conditioned on the initialization point. It shows that the variance of stochastic gradient is a decreasing function of minibatch size for linear regression and deep linear network. Compared with previous works that show similar the results for one step in SG... | SP:82228b43db88274a90aa34ff6b67f53bed74b539 |
The Impact of the Mini-batch Size on the Dynamics of SGD: Variance and Beyond | 1 INTRODUCTION . Deep learning models have achieved great success in a variety of tasks including natural language processing , computer vision , and reinforcement learning ( Goodfellow et al. , 2016 ) . Despite their practical success , there are only limited studies of the theoretical properties of deep learning ; se... | The paper shows that the variance of the gradient has an inverse dependence on the batch size in linear networks, subject to the knowledge of the initial weights. The main novelty of the paper is the computation of an exact dependence between batch size and variance of the gradient in the linear regression setting. In ... | SP:82228b43db88274a90aa34ff6b67f53bed74b539 |
Active Deep Probabilistic Subsampling | 1 INTRODUCTION . Present-day technologies produce and consume vast amounts of data , which is typically acquired using an analog-to-digital converter ( ADC ) . The amount of data digitized by an ADC is determined not only by the temporal sampling rate , but also by the manner in which spatial acquisitions are taken , e... | In this paper, the authors consider the problem of compressed sensing where the underlying signal of interest is captured and restored based only on sparse measurements: Specifically, this paper focuses on the scenario of Deep Probabilistic Subsampling (DPS) which finds sparse measurements in the way that the models de... | SP:8ef105e3182737dfc50be0d20883ab2fe2fc3610 |
Active Deep Probabilistic Subsampling | 1 INTRODUCTION . Present-day technologies produce and consume vast amounts of data , which is typically acquired using an analog-to-digital converter ( ADC ) . The amount of data digitized by an ADC is determined not only by the temporal sampling rate , but also by the manner in which spatial acquisitions are taken , e... | This paper develops methods to perform active subsampling. That is, given some downstream task like classification or image reconstruction, it sequentially selects which elements of an image or signal to sample so as to perform said task. It does so by extending the Deep Probabilistic Subsampling (DPS) method developed... | SP:8ef105e3182737dfc50be0d20883ab2fe2fc3610 |
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL | √ nH|Si| , where |Si| is the cardinality of the factored state sub- space , H is the planning horizon and n is the number of factored transitions . We also provide a lower bound , which shows near-optimality of our algorithm w.r.t . timestep T , horizon H and factored state-action subspace cardinality . Finally , as an... | The authors study the factor MDP problem in an online and episodic setting. They provide two main contributions on this question. First, they propose an OFU type algorithm which enjoys a better regret bound than Osband & Van Roy (2014) by a factor of $\sqrt{nH \Gamma}$. The improvement is brought about by a refined co... | SP:9f8f21efc3a1b9f47e34a3ecb2f5092897217362 |
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL | √ nH|Si| , where |Si| is the cardinality of the factored state sub- space , H is the planning horizon and n is the number of factored transitions . We also provide a lower bound , which shows near-optimality of our algorithm w.r.t . timestep T , horizon H and factored state-action subspace cardinality . Finally , as an... | This paper proposes a reinforcement learning algorithm FMDP-BF for episodic Factored MDPs. Similar to previous works, FMDP-BF follows the principle of "optimism in the face of uncertainty" to efficiently explore to achieve low regret. Compared to algorithms for general MDPs, FMDP-BF leverages the factorization structur... | SP:9f8f21efc3a1b9f47e34a3ecb2f5092897217362 |
Joint Perception and Control as Inference with an Object-based Implementation | 1 INTRODUCTION . Human-like computing , which aims at endowing machines with human-like perceptual , reasoning and learning abilities , has recently drawn considerable attention ( Lake , 2014 ; Lake et al. , 2015 ; Baker et al. , 2017 ) . In order to operate within a dynamic environment while preserving homeostasis ( K... | This paper proposes an extension of the RL as Inference framework, and demonstrates how to use it to express an object-centric RL model and train it on simple environments. It appears to be a combination of NEM [1] with a simple TD-learning objective on top. Results are a bit hard to interpret but seem promising. | SP:73cca0ea28b63d6d962c9a831627423947503ae7 |
Joint Perception and Control as Inference with an Object-based Implementation | 1 INTRODUCTION . Human-like computing , which aims at endowing machines with human-like perceptual , reasoning and learning abilities , has recently drawn considerable attention ( Lake , 2014 ; Lake et al. , 2015 ; Baker et al. , 2017 ) . In order to operate within a dynamic environment while preserving homeostasis ( K... | The authors propose a framework for joint perception and control as inference (PCI) to combine perception and control for the case of POMDPs. The authors particularly focus on the case of hidden perceptual states linked to small image observations, which are composed of pixels belonging to up to exactly one object each... | SP:73cca0ea28b63d6d962c9a831627423947503ae7 |
Differentiable Graph Optimization for Neural Architecture Search | 1 INTRODUCTION . Neural Architecture Search ( NAS ) methods achieve great success and outperform hand-crafted models in many deep learning applications , such as image recognition , object detection and natural language processing ( Zoph et al. , 2017 ; Liu et al. , 2019 ; Ghiasi et al. , 2019 ; Chen et al. , 2020 ) . ... | The authors address the Neural Architecture Search problem. At the core of their contribution is an architectural improvement; performance prediction of a considered architecture is much better when using a particular graph neural network on (softened) architecture topology. The rest of the NAS pipeline is naturally bu... | SP:1dd38a42ac3a5b8a7b555b8fde614f036853bec3 |
Differentiable Graph Optimization for Neural Architecture Search | 1 INTRODUCTION . Neural Architecture Search ( NAS ) methods achieve great success and outperform hand-crafted models in many deep learning applications , such as image recognition , object detection and natural language processing ( Zoph et al. , 2017 ; Liu et al. , 2019 ; Ghiasi et al. , 2019 ; Chen et al. , 2020 ) . ... | This work propose Graph Optimized Neural Architecture Learning, that uses a differentiable surrogate model to directly optimize the graph structures. More specifically, the surrogate model takes a graph structure as the neural architecture embedding and predicts a relative ranking, then applies gradient descent on the ... | SP:1dd38a42ac3a5b8a7b555b8fde614f036853bec3 |
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines | 1 INTRODUCTION . Pre-trained transformer-based masked language models such as BERT ( Devlin et al. , 2019 ) , RoBERTa ( Liu et al. , 2019 ) , and ALBERT ( Lan et al. , 2020 ) have had a dramatic impact on the NLP landscape in the recent year . The standard recipe for using such models typically involves training a pret... | This paper considers the stability of fine-tuning BERT-LARGE models, with considerations for RoBERTa and ALBERT. In particular, it aims to demonstrate that previously identified reasons, catastrophic forgetting and small fine-tuning datasets, fail to explain the observed instability. Instead, it posits that the instabi... | SP:06f1aeb9546000b8a91dd6fbdf94d3113466f9fe |
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines | 1 INTRODUCTION . Pre-trained transformer-based masked language models such as BERT ( Devlin et al. , 2019 ) , RoBERTa ( Liu et al. , 2019 ) , and ALBERT ( Lan et al. , 2020 ) have had a dramatic impact on the NLP landscape in the recent year . The standard recipe for using such models typically involves training a pret... | The paper focuses on the instability phenomenon happening in the fine-tuning of BERT-like models in downstream tasks. The reasons of such instability were assumed to be catastrophic forgetting and the small size of datasets on which being fine-tuned in previous literature. The authors conduct experiments on several sub... | SP:06f1aeb9546000b8a91dd6fbdf94d3113466f9fe |
Multi-Representation Ensemble in Few-Shot Learning | Deep neural networks ( DNNs ) compute representations in a layer by layer fashion , producing a final representation at the top layer of the pipeline , and classification or regression is made using the final representation . A number of DNNs ( e.g. , ResNet , DenseNet ) have shown that representations from the earlier... | This paper presents a deeply supervised few-shot learning model via ensemble achieving state-of-the-art performance on mini-ImageNet and tiredImageNet. The authors first studied the classification accuracy on mini-Image across convolutional layers and found the network could perform well even in the middle layer. There... | SP:551d4ab2faa435d4f352efc6109525f0a1a5510c |
Multi-Representation Ensemble in Few-Shot Learning | Deep neural networks ( DNNs ) compute representations in a layer by layer fashion , producing a final representation at the top layer of the pipeline , and classification or regression is made using the final representation . A number of DNNs ( e.g. , ResNet , DenseNet ) have shown that representations from the earlier... | Thanks to the authors for providing such an ensemble approach. This paper aims to find a way to directly utilize representations with the classification layer(s) to obtain better performance. The ensemble method is able to create an ensemble of classifiers. And the ensemble achieves the new state-of-the-art results in... | SP:551d4ab2faa435d4f352efc6109525f0a1a5510c |
Novel Policy Seeking with Constrained Optimization | 1 INTRODUCTION . In the sense of learning through interactions with the environment , the scheme of reinforcement learning ( RL ) is conceptually similar to the emergence of intelligence ( Sutton et al. , 1998 ) : an agent explores and exploits information of a given environment , learns to master some certain skills t... | This paper aims at novel policy seeking which incorporates curiosity-driven exploration for better reinforcement learning. This paper first propose to use a Wasserstein-based metric to calculate the difference between policies, and use it to define the policy novelty. With these, the authors modeled the novel policy ... | SP:f7b7dfafb03090a2c940ba738234a6c80bd5ad0e |
Novel Policy Seeking with Constrained Optimization | 1 INTRODUCTION . In the sense of learning through interactions with the environment , the scheme of reinforcement learning ( RL ) is conceptually similar to the emergence of intelligence ( Sutton et al. , 1998 ) : an agent explores and exploits information of a given environment , learns to master some certain skills t... | This paper proposed a method to leverage the constrained optimization for policy training to learn diverse policies given some references. Based on a diversity metric defined on policy divergences, the paper employs two constrained optimization techniques for this problem with some modifications. Experiments on mujoco ... | SP:f7b7dfafb03090a2c940ba738234a6c80bd5ad0e |
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has seen increased interest in recent years due to its ability to have neural-network-based agents learn to act in environments through interactions . For continuous control tasks , on-policy algorithms such as REINFORCE [ 2 ] , TRPO [ 10 ] , A3C [ 14 ] , PPO [ 17 ] a... | The paper presents an empirical evaluation of many algorithmic choices made in the implementations of on-policy actor-critic algorithms in deep reinforcement learning (RL). The authors group those choices in clusters in which they expect some interactions. For each cluster, they test sets of randomly made choices while... | SP:41f93461a907d77fc2e0f4f4e3e89a0e7a133736 |
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has seen increased interest in recent years due to its ability to have neural-network-based agents learn to act in environments through interactions . For continuous control tasks , on-policy algorithms such as REINFORCE [ 2 ] , TRPO [ 10 ] , A3C [ 14 ] , PPO [ 17 ] a... | This paper carries out a large-scale study for understanding of on-policy deep actor-critic. The study looks into a large choices of many implementation settings and design decisions, and investigate their impact on the task performance. The evaluations are done with 250000 RL agents on 5 different continuous control t... | SP:41f93461a907d77fc2e0f4f4e3e89a0e7a133736 |
CIGMO: Learning categorical invariant deep generative models from grouped data | 1 INTRODUCTION . In everyday life , we see objects in a great variety . Categories of objects are numerous and their shape variations are tremendously rich ; different views make an object look totally different ( Figure 1 ( A ) ) . Recent neuroscientific studies have revealed how the primate brain organizes representa... | This work proposes a probabilistic model which disentangles view, class and shape attributes explicitly (it does not rely on an emergent phenomena of disentangled factors in the latent space). In comparison to a similar approach, GVAE, CIGMO additionally disentangles the content factor into category and shape factors. ... | SP:c4eef128786551f4e0ec9f43853df8b59a04f205 |
CIGMO: Learning categorical invariant deep generative models from grouped data | 1 INTRODUCTION . In everyday life , we see objects in a great variety . Categories of objects are numerous and their shape variations are tremendously rich ; different views make an object look totally different ( Figure 1 ( A ) ) . Recent neuroscientific studies have revealed how the primate brain organizes representa... | This paper proposes a categorical invariant generative model (CIGMO) from a set of 2D images that tries to disentangle the factors of data category, intra-category geometry, and rendering viewpoint. CIGMO trains a VAE with a hierarchical graphical model that explicitly factors out the three components by design. Experi... | SP:c4eef128786551f4e0ec9f43853df8b59a04f205 |
A General Computational Framework to Measure the Expressiveness of Complex Networks using a Tight Upper Bound of Linear Regions | 1 INTRODUCTION . Deep nerual network ( DNN ) ( LeCun et al. , 2015 ) has obtained great success in many fields such as computer vision , speech recognition and neural language process ( Krizhevsky et al. , 2012 ; Hinton et al. , 2012 ; Devlin et al. , 2018 ; Goodfellow et al. , 2014 ) . However , it has not been comple... | This paper extends on the framework of matrix computation in Hinz & Van d Geer (2019) to give a tight upper bound for linear regions. In particular, the paper shows improvement over the bounds derived in Serra et al. 2018 and extends the bounds for more complex networks with skip and residual connections. The paper als... | SP:48df1a6af80b29c4c38439bda1d69472ade37f2c |
A General Computational Framework to Measure the Expressiveness of Complex Networks using a Tight Upper Bound of Linear Regions | 1 INTRODUCTION . Deep nerual network ( DNN ) ( LeCun et al. , 2015 ) has obtained great success in many fields such as computer vision , speech recognition and neural language process ( Krizhevsky et al. , 2012 ; Hinton et al. , 2012 ; Devlin et al. , 2018 ; Goodfellow et al. , 2014 ) . However , it has not been comple... | This paper studies the counting of linear regions of a multi-layer ReLU network and gives an upper bound on the number of linear regions that is tighter than existing results. Networks with skip connections and pooling layers are also considered. The authors then compare their bounds for standard multi-layer networks a... | SP:48df1a6af80b29c4c38439bda1d69472ade37f2c |
NASOA: Towards Faster Task-oriented Online Fine-tuning | 1 INTRODUCTION . Fine-tuning using pre-trained models becomes the de-facto standard in the field of computer vision because of its impressive results on various downstream tasks such as fine-grained image classification ( Nilsback & Zisserman , 2008 ; Welinder et al. , 2010 ) , object detection ( He et al. , 2019 ; Jia... | In this paper, a joint Neural Architecture Search and Online Adaption (NASOA) framework is proposed to achieve a faster task-oriented fine-tuning upon the request of users. In particular, two main contributions are made in this paper: (1) A fine-tuning pipeline that seamlessly combines the training-efficient NAS and on... | SP:4fe317ebc002abdec341b978f730b3dcaa58b9cf |
NASOA: Towards Faster Task-oriented Online Fine-tuning | 1 INTRODUCTION . Fine-tuning using pre-trained models becomes the de-facto standard in the field of computer vision because of its impressive results on various downstream tasks such as fine-grained image classification ( Nilsback & Zisserman , 2008 ; Welinder et al. , 2010 ) , object detection ( He et al. , 2019 ; Jia... | This works aims at task-oriented fine-tuning from pre-trained ImageNet models. It proposes a Neural Architecture Search and Online Adaption framework (NASOA) to perform fast task-oriented model fine-tuning. The NASOA first employ an offline NAS to select a group of models and then pick up the most suitable model from t... | SP:4fe317ebc002abdec341b978f730b3dcaa58b9cf |
Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero | 1 INTRODUCTION . There is no polynomial-time algorithm found for NP-hard problems [ 7 ] , but they often arise in many real-world optimization tasks . Therefore , a variety of algorithms have been developed in a long history , including approximation algorithms [ 2 , 14 ] , meta-heuristics based on local searches such ... | This paper proposes an AlphaGo Zero style algorithm for training policies for solving combinatorial optimization problems. The main idea is to generate training data with MCTS for both a policy network and a value network via self-play. Different graph neural networks (GNNs) are considered as learning models to compare... | SP:5c1ba72542bd2c3ebac7e96f88fb48599d725b6b |
Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero | 1 INTRODUCTION . There is no polynomial-time algorithm found for NP-hard problems [ 7 ] , but they often arise in many real-world optimization tasks . Therefore , a variety of algorithms have been developed in a long history , including approximation algorithms [ 2 , 14 ] , meta-heuristics based on local searches such ... | There has been a sequence of recent works on learning heuristics for combinatorial optimization problems on graphs by treating them as Markov decision processes, and learning by reinforcement a good policy. Since the dynamics of these problems can be readily simulated, in this paper, the authors propose to use AlphaZer... | SP:5c1ba72542bd2c3ebac7e96f88fb48599d725b6b |
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching | 1 INTRODUCTION . Machine learning models have quickly become the backbone of many applications from photo processing on mobile devices and ad placement to security and surveillance ( LeCun et al. , 2015 ) . These applications often rely on large training datasets that aggregate samples of unknown origins , and the secu... | This paper introduces a novel targeted clean-label poisoning attack, expected to be more efficient and scalable than current ones. The attack is formulated as a bilevel problem which is then solved with a (fast) heuristic approach based on aligning the gradients of the inner and outer objective functions. A theoretical... | SP:758e3d852e162f93a9984eac06c0b23cd67bc727 |
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching | 1 INTRODUCTION . Machine learning models have quickly become the backbone of many applications from photo processing on mobile devices and ad placement to security and surveillance ( LeCun et al. , 2015 ) . These applications often rely on large training datasets that aggregate samples of unknown origins , and the secu... | This paper proposed a simple yet effective approach for data poisoning attack targeting a few "clean-label" victim images, using the idea of gradient matching (cosine similarity maximization) between the gradients of adversarial and clean losses. Although the attack model still requires knowing the network architecture... | SP:758e3d852e162f93a9984eac06c0b23cd67bc727 |
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning | 1 INTRODUCTION . Learning in multiagent settings is inherently more difficult than single-agent learning because an agent interacts both with the environment and other agents ( Buşoniu et al. , 2010 ) . Specifically , the fundamental challenge in multiagent reinforcement learning ( MARL ) is the difficulty of learning... | This paper studies meta-learning in multi-agent reinforcement learning. It proposes a meta multi-agent policy gradient method that considers the learning processes of other agents in the environment for fast adaptation. This method can be seen as a unified framework of previous methods (Al-Shedivat et al. (2018) and Fo... | SP:90b93c165039046b77b5c3ee1df5b1090bfd0f42 |
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning | 1 INTRODUCTION . Learning in multiagent settings is inherently more difficult than single-agent learning because an agent interacts both with the environment and other agents ( Buşoniu et al. , 2010 ) . Specifically , the fundamental challenge in multiagent reinforcement learning ( MARL ) is the difficulty of learning... | This paper points out that a key challenge in MARL is the non-stationarity of other agents' policies, as opposed to previous papers which only account for non-stationarity of the environment. The paper extends (Al-Shedivat et al., 2018) by directly conditioning the meta-policy on a distribution of other agents' policie... | SP:90b93c165039046b77b5c3ee1df5b1090bfd0f42 |
On Proximal Policy Optimization's Heavy-Tailed Gradients | 1 INTRODUCTION . As Deep Reinforcement Learning ( DRL ) methods have made strides on such diverse tasks as game playing and continuous control ( Berner et al. , 2019 ; Silver et al. , 2017 ; Mnih et al. , 2015 ) , policy gradient methods ( Williams , 1992 ; Sutton et al. , 2000 ; Mnih et al. , 2016 ) have emerged as a ... | This paper presents an intriguing analysis of the gradient distributions over the course of training for popular RL algorithms in common mujoco benchmarks. The observation that negative advantages are a bigger contributor to the kurtosis than positive advantages seems interesting and if true as a general phenomenon, d... | SP:3fc873bd47448de58d54f7def16a1ddb7df613b8 |
On Proximal Policy Optimization's Heavy-Tailed Gradients | 1 INTRODUCTION . As Deep Reinforcement Learning ( DRL ) methods have made strides on such diverse tasks as game playing and continuous control ( Berner et al. , 2019 ; Silver et al. , 2017 ; Mnih et al. , 2015 ) , policy gradient methods ( Williams , 1992 ; Sutton et al. , 2000 ; Mnih et al. , 2016 ) have emerged as a ... | This paper performs an empirical analysis of the heavy-tailedness of the PPO gradients across MuJoCo environments. They find that PPO gradients are heavy-tailed, which means that they are sensitive to outliers, which means computing the expected gradient is hard. The paper studies two causes of this issue -- advantage ... | SP:3fc873bd47448de58d54f7def16a1ddb7df613b8 |
Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection | 1 INTRODUCTION . An autoencoder ( Rumelhart et al. , 1986 ) is a neural network trained to reconstruct samples from a training data distribution . As the quality of reconstruction is expected to degrade for inputs that are significantly different from training data , autoencoders are widely used in outlier detection ( ... | The paper describes a new method for detecting outliers with deep autoencoders by suppressing the reconstruction of out-of-distribution data. The article first investigates the reasons why standard autoencoders (AEs) reconstruct outlier datapoints fairly well, and are therefore problematic when used to detect anomalies... | SP:ce02279c05b6c1d89e80f8d2ce16403aa2586ad6 |
Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection | 1 INTRODUCTION . An autoencoder ( Rumelhart et al. , 1986 ) is a neural network trained to reconstruct samples from a training data distribution . As the quality of reconstruction is expected to degrade for inputs that are significantly different from training data , autoencoders are widely used in outlier detection ( ... | The authors address an important problem of autoencoders having low reconstruction error for OOD instances. They use the regular inlier reconstruction loss minimization with an additional term to maximize reconstruction loss for fake sampled OOD instances. A two stage Langevin Monte Carlo sampling technique is used for... | SP:ce02279c05b6c1d89e80f8d2ce16403aa2586ad6 |
C-Learning: Learning to Achieve Goals via Recursive Classification | 1 INTRODUCTION . In this paper , we aim to reframe the goal-conditioned reinforcement learning ( RL ) problem as one of predicting and controlling the future state of the world . This reframing is useful not only because it suggests a new algorithm for goal-conditioned RL , but also because it explains a commonly used ... | The authors propose a new algorithm, called C-learning, which tackles goal-conditioned reinforcement learning problems. Specifically, the algorithm converts the future density estimation problem, which goal-conditioned Q learning is inherently performing, to a classification problem. The experiments showed that the mod... | SP:14f7771d7e9c3d3f728ae24720d22847b08ac7a1 |
C-Learning: Learning to Achieve Goals via Recursive Classification | 1 INTRODUCTION . In this paper , we aim to reframe the goal-conditioned reinforcement learning ( RL ) problem as one of predicting and controlling the future state of the world . This reframing is useful not only because it suggests a new algorithm for goal-conditioned RL , but also because it explains a commonly used ... | This paper studies to predict future state density function by using an indirectly method via classification. The main idea is to sample the future state from two sources: 1) from replay buffer, 2) the actual next state in the trajectory (in off policy setting we only need the next state) and then use a classifier to d... | SP:14f7771d7e9c3d3f728ae24720d22847b08ac7a1 |
Gradient Projection Memory for Continual Learning | 1 INTRODUCTION . Humans exhibit remarkable ability in continual adaptation and learning new tasks throughout their lifetime while maintaining the knowledge gained from past experiences . In stark contrast , Artificial Neural Networks ( ANNs ) under such Continual Learning ( CL ) paradigm ( Ring , 1998 ; Thrun & Mitchel... | This work targets learning multi-class classifiers in the continual learning setting. The key idea is to learn new tasks by taking gradient steps in directions orthogonal to the gradient subspaces marked as crucial for previous past tasks. The method employs SVD after learning each task to find the crucial subspaces (w... | SP:4df83f99f68580ffe805e3826ea01f4a0c5dc523 |
Gradient Projection Memory for Continual Learning | 1 INTRODUCTION . Humans exhibit remarkable ability in continual adaptation and learning new tasks throughout their lifetime while maintaining the knowledge gained from past experiences . In stark contrast , Artificial Neural Networks ( ANNs ) under such Continual Learning ( CL ) paradigm ( Ring , 1998 ; Thrun & Mitchel... | The paper proposes one of the most scalable approaches to sequential continual learning with known task boundaries and related tasks, while taking steps towards enforcing data privacy and removing some of the task label constraints. At all levels in expressive deep models, SVD is used on learned representations to iden... | SP:4df83f99f68580ffe805e3826ea01f4a0c5dc523 |
Intrinsically Guided Exploration in Meta Reinforcement Learning | 1 INTRODUCTION . Human intelligence is able to transfer knowledge across tasks and acquire new skills within limited experiences . However , most reinforcement learning ( RL ) agents still require large amounts of data to achieve human-level performance ( Silver et al. , 2017 ; Hessel et al. , 2018 ; Vinyals et al. , 2... | The paper presents a method for efficient task identification to improve adaptation in a meta RL setting. The approach is based on learning an exploration policy to quickly discriminate the task at hand, so that to leverage a task-specific policy for exploitation. To do so, it employs an intrinsic reward proportional t... | SP:ec463ec2a5bde3efb2f9daa1fd29dd71c7472341 |
Intrinsically Guided Exploration in Meta Reinforcement Learning | 1 INTRODUCTION . Human intelligence is able to transfer knowledge across tasks and acquire new skills within limited experiences . However , most reinforcement learning ( RL ) agents still require large amounts of data to achieve human-level performance ( Silver et al. , 2017 ; Hessel et al. , 2018 ; Vinyals et al. , 2... | This paper proposes a novel meta-learning method, aimed at solving the following problem: at test time, the agent has N episodes to gather information [exploration phase], and we care about its return in the N+1th episode [exploitation phase]. To this end, the authors propose to learn a separate exploration and exploit... | SP:ec463ec2a5bde3efb2f9daa1fd29dd71c7472341 |
Feature Integration and Group Transformers for Action Proposal Generation | 1 INTRODUCTION . Owing to the fast development of digital cameras and online video services , the rapid growth of video sequences encourages the research of video content analysis . The applications of interest include video summarization ( Yao et al. , 2015 ; 2016 ) , captioning ( Chen et al. , 2019a ; Chen & Jiang , ... | This paper tackles the problem of temporal action proposal generation (TAPG). The authors address the problem from two perspectives: features wise and score fusion wise. They use non-local blocks to integrate appearance features and motion features together. For score fusion, they propose transformer based module to in... | SP:aebc7fd9042d95e9ee3d1baf909b5267e0a10775 |
Feature Integration and Group Transformers for Action Proposal Generation | 1 INTRODUCTION . Owing to the fast development of digital cameras and online video services , the rapid growth of video sequences encourages the research of video content analysis . The applications of interest include video summarization ( Yao et al. , 2015 ; 2016 ) , captioning ( Chen et al. , 2019a ; Chen & Jiang , ... | In general, it is an interesting paper to utilize multiple techniques to enhance two-stream features and transformer to improve proposal scores, though all the techniques are not first proposed in this paper. But some technical details are not clearly presented, so the solidarity cannot be evaluated. Furturemore, more ... | SP:aebc7fd9042d95e9ee3d1baf909b5267e0a10775 |
Revealing the Structure of Deep Neural Networks via Convex Duality | 1 INTRODUCTION . Deep neural networks ( DNNs ) have become extremely popular due to their success in machine learning applications . Even though DNNs are highly over-parameterized and non-convex , simple first-order algorithms , e.g. , Stochastic Gradient Descent ( SGD ) , can be used to successfully train them . Moreo... | The authors consider training neural networks with a variety of losses and regularization (such as weight decay). The authors introduce a novel convex-dual formulation which allows them to characterize optimal solutions as being extreme points of particular convex sets. For multi-layer linear networks, the authors ... | SP:ec03a452d165bcff98d9e40050a495dc1f30255b |
Revealing the Structure of Deep Neural Networks via Convex Duality | 1 INTRODUCTION . Deep neural networks ( DNNs ) have become extremely popular due to their success in machine learning applications . Even though DNNs are highly over-parameterized and non-convex , simple first-order algorithms , e.g. , Stochastic Gradient Descent ( SGD ) , can be used to successfully train them . Moreo... | This work uses dual formulations of Neural Networks with ReLU activations. It starts explaining the dual formulations with simplest single layer unregularised linear neural networks with a single dimensional output layer. Then gradually extends the models to deep, regularised models with ReLU activations. There is also... | SP:ec03a452d165bcff98d9e40050a495dc1f30255b |
Saliency is a Possible Red Herring When Diagnosing Poor Generalization | 1 INTRODUCTION . A fundamental challenge when applying deep learning models stems from poor generalization due to covariate shift ( Moreno-Torres et al. , 2012 ) when the probably approximately correct ( PAC ) learning i.i.d . assumption is invalid ( Valiant , 1984 ) i.e . the training distribution is different from th... | The reviewed paper explores the relationship between the quality and spatial distribution of the saliency maps produced at inference time and the model's generalization performance. The authors employed a number of existing methods as well as proposed and implemented their own technique (ActDiff) to align saliency maps... | SP:88181a6db5701fbd9a096e08d5f892d6c1bea0e9 |
Saliency is a Possible Red Herring When Diagnosing Poor Generalization | 1 INTRODUCTION . A fundamental challenge when applying deep learning models stems from poor generalization due to covariate shift ( Moreno-Torres et al. , 2012 ) when the probably approximately correct ( PAC ) learning i.i.d . assumption is invalid ( Valiant , 1984 ) i.e . the training distribution is different from th... | This paper focuses on the confounder problem that spatially-seperated image regions (e.g. shoulders of xray images) might spuriously correlated with the target (e.g. pneumonia). If given a human-labeled region that is deemed important, we can decrease this spuriousness by regularizing the model toward the important reg... | SP:88181a6db5701fbd9a096e08d5f892d6c1bea0e9 |
Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines | Despite their ubiquity in core AI fields like natural language processing , the mechanics of deep attention-based neural networks like the “ Transformer ” model are not fully understood . In this article , we present a new perspective towards understanding how Transformers work . In particular , we show that the “ dot-... | The paper aims at providing a mathematical structure for explaining the mechanism behind the attention block characteristic to transformers. The focus of the paper is on the scaled dot-product attention, reviewed in Eq. (1). In my understanding, the whole mechanism can be seen as an instance of the set kernel. The inpu... | SP:48e591a5fa2802fc4bb0c7100d120ff860b074f3 |
Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines | Despite their ubiquity in core AI fields like natural language processing , the mechanics of deep attention-based neural networks like the “ Transformer ” model are not fully understood . In this article , we present a new perspective towards understanding how Transformers work . In particular , we show that the “ dot-... | In this paper, the authors treat a particular Transformer, "dot-product attention", as an RKBS kernel called "exponentiated query-key kernel". The explicit form of feature maps and Bach space are given. Moreover, authors term a binary kernel learning problems within the framework of regularized empirical risk minimiza... | SP:48e591a5fa2802fc4bb0c7100d120ff860b074f3 |
A Block Minifloat Representation for Training Deep Neural Networks | 1 INTRODUCTION . The energy consumption and execution time associated with training Deep Neural Networks ( DNNs ) is directly related to the precision of the underlying numerical representation . Most commercial accelerators , such as NVIDIA Graphics Processing Units ( GPUs ) , employ conventional floating-point repres... | The authors proposes block-minifloat (BM), a floating-point format for DNN training. BM is a fairly simple extension to block floating-point (BFP), which was proposed in (Drumond 2018 and Yang 2019). In BFP, a block of integer mantissas share a single exponent. In BM, a block of narrow floats share a single exponent bi... | SP:2cabed9c97692e64a609bb5a66fe7505c53c59fb |
A Block Minifloat Representation for Training Deep Neural Networks | 1 INTRODUCTION . The energy consumption and execution time associated with training Deep Neural Networks ( DNNs ) is directly related to the precision of the underlying numerical representation . Most commercial accelerators , such as NVIDIA Graphics Processing Units ( GPUs ) , employ conventional floating-point repres... | This paper introduced a new representation (Block Minifloat) for training DNNs with low precisions of 8-bit or less. This new representation combines FP8 formats and the shared exponent bias concept to cover the dynamic range of tensors needed for DNN training. Compared to other published FP8 format, this representatio... | SP:2cabed9c97692e64a609bb5a66fe7505c53c59fb |
Transferable Unsupervised Robust Representation Learning | 1 INTRODUCTION . Recently , there has been an increasing interest in unsupervised visual representation learning , where the goal is to learn effective representations of images without using human annotations ( Bachman et al. , 2019 ; Chen et al. , 2020b ; He et al. , 2020 ; Misra & Maaten , 2020 ) . In this work , we... | This paper uses a different data augmentation (AugMix) scheme to improve self-supervised representation learning. It improves accuracy and (corruption and adversarial) robustness by a sufficiently interesting amount. The paper's presentation is clear, but the paper could be more thorough. Since the technique is simple ... | SP:5d875376d1c68281008c7a22abcf09b968afb841 |
Transferable Unsupervised Robust Representation Learning | 1 INTRODUCTION . Recently , there has been an increasing interest in unsupervised visual representation learning , where the goal is to learn effective representations of images without using human annotations ( Bachman et al. , 2019 ; Chen et al. , 2020b ; He et al. , 2020 ; Misra & Maaten , 2020 ) . In this work , we... | This paper proposes *Unsupervised Robust Representation Learning* (URRL), a framework that combines several data augmentation schemes and a similarity-based loss. The goal is to improve the robustness of visual representations to image perturbations. A further goal is to maintain the robustness properties of pre-traine... | SP:5d875376d1c68281008c7a22abcf09b968afb841 |
PABI: A Unified PAC-Bayesian Informativeness Measure for Incidental Supervision Signals | 1 INTRODUCTION . The supervised learning paradigm , where direct supervision signals are assumed to be available in high-quality and large amounts , has been struggling to fulfill the needs in many real-world AI applications . As a result , researchers and practitioners often resort to datasets that are not collected d... | This paper proposes a unified PAC-Bayesian-based informativeness measure (PABI) to quantify the value of incidental signals. PABI can measure various types of incidental signals such as partial labels, noisy labels, constraints, auxiliary signals, cross-domain signals, and their combinations. In NER and QA tasks, they ... | SP:f952feef70a17987e9691ce2faef013e1b59168e |
PABI: A Unified PAC-Bayesian Informativeness Measure for Incidental Supervision Signals | 1 INTRODUCTION . The supervised learning paradigm , where direct supervision signals are assumed to be available in high-quality and large amounts , has been struggling to fulfill the needs in many real-world AI applications . As a result , researchers and practitioners often resort to datasets that are not collected d... | This paper proposes PABI (PAC-Bayesian Informativeness?), a way of measuring and predicting the usefulness of “incidental supervision signal” for a downstream classification task. In particular, when labeled data is only available in noisy or partial form, or over a different domain than the target test domain, this da... | SP:f952feef70a17987e9691ce2faef013e1b59168e |
Self-Activating Neural Ensembles for Continual Reinforcement Learning | 1 INTRODUCTION . Lifelong learning is of critical importance for the field of robotics ; an agent that interacts with the world should be able to continuously learn from it , acting intelligently in a wide variety of situations . In marked contrast to this ideal , most standard deep reinforcement learning methods are c... | The paper proposes SANE -- an architecture and a training algorithm for continual learning. The SANE model consists of a tree where each node can act as an RL agent and where nodes act according to the dispatching mechanism based on their reward prediction. This allows to activate and update only those agents that are ... | SP:6fbc712869c021f261ee4e71e49a9043f8191925 |
Self-Activating Neural Ensembles for Continual Reinforcement Learning | 1 INTRODUCTION . Lifelong learning is of critical importance for the field of robotics ; an agent that interacts with the world should be able to continuously learn from it , acting intelligently in a wide variety of situations . In marked contrast to this ideal , most standard deep reinforcement learning methods are c... | This work addresses multi-task learning where task boundaries are unknown. The approach is to construct a dynamic decision tree with nodes made up of small networks. Nodes are merged and promoted in the tree based on learned error bounds on value function estimates. Inference through the tree works by selecting nodes w... | SP:6fbc712869c021f261ee4e71e49a9043f8191925 |
Efficient Estimators for Heavy-Tailed Machine Learning | 1 INTRODUCTION . Existing estimators in machine learning are largely designed for “ thin-tailed ” data , such as those coming from a Gaussian distribution . Past work in statistical estimation has given sufficient evidence that in the absence of these “ thin-tails ” , classical estimators based on minimizing the empiri... | The author(s) propose a computationally efficient mean estimator for generative distribution that are "heavy-tailed" in nature. The phenomenon of heavy tailed distributions for gradients in the training stage of generative models are common in nature and the proposed method aims to alleviate this problem by constructin... | SP:cc400541c428521afdffeede69807daf3180cb17 |
Efficient Estimators for Heavy-Tailed Machine Learning | 1 INTRODUCTION . Existing estimators in machine learning are largely designed for “ thin-tailed ” data , such as those coming from a Gaussian distribution . Past work in statistical estimation has given sufficient evidence that in the absence of these “ thin-tails ” , classical estimators based on minimizing the empiri... | The paper studies the problem of high-probability mean estimation for heavy-tailed distributions, i.e., constructing a high-probability confidence intervals for the mean, when the underlying distribution has only finite low-degree moments. The paper motivates this problem from the view-point of machine learning algori... | SP:cc400541c428521afdffeede69807daf3180cb17 |
Theoretical bounds on estimation error for meta-learning | 1 INTRODUCTION . Many practical machine learning applications deal with distributional shift from training to testing . One example is few-shot classification ( Ravi & Larochelle , 2016 ; Vinyals et al. , 2016 ) , where new classes need to be learned at test time based on only a few examples for each novel class . Rece... | The paper studies the information-theoretic lower bounds in the minimax setting of meta-learning. The paper also discusses upper and lower bounds in the hierarchical Bayesian framework of meta linear regression. The novelty of the paper is two-fold: a) it proves a novel meta-learning local packing result to compute the... | SP:ea57214b79bbaaa7538597610944256e6ac1fbe4 |
Theoretical bounds on estimation error for meta-learning | 1 INTRODUCTION . Many practical machine learning applications deal with distributional shift from training to testing . One example is few-shot classification ( Ravi & Larochelle , 2016 ; Vinyals et al. , 2016 ) , where new classes need to be learned at test time based on only a few examples for each novel class . Rece... | This paper provides a minimax novel-task risk lower bound for meta learning via information-theoretical techniques, showing the fundamental limits of meta learning. The novel-task minimax risk depends on the number of samples from the meta-training set and novel task, as well as the task similarity. The authors further... | SP:ea57214b79bbaaa7538597610944256e6ac1fbe4 |
No MCMC for me: Amortized sampling for fast and stable training of energy-based models | 1 INTRODUCTION . Energy-Based Models ( EBMs ) have recently regained popularity within machine learning , partly inspired by the impressive results of Du & Mordatch ( 2019 ) and Song & Ermon ( 2020 ) on largescale image generation . Beyond image generation , EBMs have also been successfully applied to a wide variety of... | This paper proposes a new method on training energy-based models with maximum likelihood. Instead of using MCMC approaches to sample from the EBM, authors follow previous work on training neural generators for faster sample generation. In particular, authors consider a special generator where the output is convolved wi... | SP:80d95638850c9ff81a9b271d28a8aecf238497e6 |
No MCMC for me: Amortized sampling for fast and stable training of energy-based models | 1 INTRODUCTION . Energy-Based Models ( EBMs ) have recently regained popularity within machine learning , partly inspired by the impressive results of Du & Mordatch ( 2019 ) and Song & Ermon ( 2020 ) on largescale image generation . Beyond image generation , EBMs have also been successfully applied to a wide variety of... | This paper presents a method for improving training of energy-based models. Rather than drawing samples using persistent contrastive divergence / MCMC, this approach parameterizes a separate model, which is trained to directly output samples. This effectively adds an additional KL divergence to the objective. The autho... | SP:80d95638850c9ff81a9b271d28a8aecf238497e6 |
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition | 1 INTRODUCTION . Acquiring predictive features is critical for building trustworthy machine learning systems , but this often comes at a daunting cost . Such a cost can be in the form of energy needed to maintain an ambient sensor ( Ardywibowo et al. , 2019 ; Yang et al. , 2020 ) , time needed to complete an experiment... | This paper proposes an RNN model for adaptive dynamic feature selection, for efficient and interpretable human activity recognition (HAR). From the intuition that human activity can be predictable by using a small number of sensors, the paper introduces an l0-norm minimization problem with parameter regularization, and... | SP:06417327dae11b539a7e6087a8d792ccd729a74a |
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition | 1 INTRODUCTION . Acquiring predictive features is critical for building trustworthy machine learning systems , but this often comes at a daunting cost . Such a cost can be in the form of energy needed to maintain an ambient sensor ( Ardywibowo et al. , 2019 ; Yang et al. , 2020 ) , time needed to complete an experiment... | The authors provide a novel combination of known architectures to an important use case of reducing the density of required measurements in sensor-fusion based temporal multi-class inference tasks. This has implications in energy consumptions of wearable sensors. but could even generalise to measurement timings in cl... | SP:06417327dae11b539a7e6087a8d792ccd729a74a |
Evaluating Online Continual Learning with CALM | 1 INTRODUCTION . Machines , like humans , can learn to perform multiple different tasks from feedback alone ( Caruana , 1997 ) . On the other hand , humans , but not machines , can benefit from settings in which tasks are presented repeatedly for multiple trials before switching to the next one ( Flesch et al. , 2018 )... | This paper’s main contributions are (i) to propose two new benchmarks for online continual learning in the context of language modelling and (ii) evaluate the performance of a number of composition-of-experts-based models on the new datasets using a number of metrics. The multilingual benchmark, derived from an existin... | SP:2dbd99bd7ef55248d5d6fcca7ae2866218173949 |
Evaluating Online Continual Learning with CALM | 1 INTRODUCTION . Machines , like humans , can learn to perform multiple different tasks from feedback alone ( Caruana , 1997 ) . On the other hand , humans , but not machines , can benefit from settings in which tasks are presented repeatedly for multiple trials before switching to the next one ( Flesch et al. , 2018 )... | The paper proposes two benchmarks for continual language modeling: one evaluating character-level multilingual drift between languages which share similar characters and second evaluating word-level drift between English corpora of different domains. The setup is online in the sense of evaluation: they evaluate on the ... | SP:2dbd99bd7ef55248d5d6fcca7ae2866218173949 |
Luring of transferable adversarial perturbations in the black-box paradigm | 1 INTRODUCTION . Neural networks based systems have been shown to be vulnerable to adversarial examples ( Szegedy et al. , 2014 ) , i.e . maliciously modified inputs that fool a model at inference time . Many directions have been explored to explain and characterize this phenomenon ( Schmidt et al. , 2018 ; Ford et al.... | The paper proposes a new framework for addressing the problem of adversaries in black box settings in order to improve model robustness. Leveraging classical deception frameworks used in network security, the authors propose to fool the attacker by training what they call a `luring component’ that is augmented to an al... | SP:e59dce35ddf8c0356e92c60959da89c7f4ce20de |
Luring of transferable adversarial perturbations in the black-box paradigm | 1 INTRODUCTION . Neural networks based systems have been shown to be vulnerable to adversarial examples ( Szegedy et al. , 2014 ) , i.e . maliciously modified inputs that fool a model at inference time . Many directions have been explored to explain and characterize this phenomenon ( Schmidt et al. , 2018 ; Ford et al.... | In this paper, the authors present a novel approach to evade the transferability of adversarial examples between two models. Specifically, they design a luring loss to train model T, an augmented version of M, where the adversarial examples cannot transfer from T to M. The luring loss is designed to reach a twofold obj... | SP:e59dce35ddf8c0356e92c60959da89c7f4ce20de |
Trans-Caps: Transformer Capsule Networks with Self-attention Routing | 1 INTRODUCTION . Convolutional Neural Networks ( CNNs ) have achieved state-of-the-art performance in many different computer vision tasks ( Krizhevsky et al. , 2012 ; He et al. , 2016 ) . This is achieved by local connectivity and parameter sharing across spatial locations so that useful local features learned in one ... | The paper proposes to swap the typical routing mechanisms in capsules for a more standard attention mechanism. The attention mechanism is based on computing similarity scores using gaussians instead of dot-products . The authors show that this leads to better downstream performance of more natural tasks while preservin... | SP:e3aa12a5f1d70e4d877ae9ff02a92981c92f1f32 |
Trans-Caps: Transformer Capsule Networks with Self-attention Routing | 1 INTRODUCTION . Convolutional Neural Networks ( CNNs ) have achieved state-of-the-art performance in many different computer vision tasks ( Krizhevsky et al. , 2012 ; He et al. , 2016 ) . This is achieved by local connectivity and parameter sharing across spatial locations so that useful local features learned in one ... | The submission details a novel technique to learn the routing in capsule networks for image classification tasks. Connecting capsules in such architectures typically requires iterative approaches which are computationally expensive. The main idea in this submission is to leverage a non-iterative attention mechanism to ... | SP:e3aa12a5f1d70e4d877ae9ff02a92981c92f1f32 |
Factoring out Prior Knowledge from Low-Dimensional Embeddings | 1 INTRODUCTION . Embedding high dimensional data into low dimensional spaces , such as with tSNE ( van der Maaten & Hinton , 2008 ) or UMAP ( McInnes et al. , 2018 ) , allow us to visually inspect and discover meaningful structure from the data that would otherwise be difficult or impossible to see . These methods are ... | The author(s) provide two methods for factoring out specific covariates from tSNE, UMAP or other distance matrices. The first one is JEDI, an extension of tSNE that minimizes a parameterized divergence (that takes into account the information to be factored out) instead of the simple KL divergence between high dimensio... | SP:63a12d3b46928bf6fece89f1a1a51579ab162d84 |
Factoring out Prior Knowledge from Low-Dimensional Embeddings | 1 INTRODUCTION . Embedding high dimensional data into low dimensional spaces , such as with tSNE ( van der Maaten & Hinton , 2008 ) or UMAP ( McInnes et al. , 2018 ) , allow us to visually inspect and discover meaningful structure from the data that would otherwise be difficult or impossible to see . These methods are ... | The distance metric learned by low-dimensional embeddings typically captures the knowledge that we already know. This paper proposes a principled way of factoring out prior knowledge (in the form of distance matrices) from tSNE and UMAP embeddings. Two algorithms are proposed for factoring out prior knowledge. JEDI (fo... | SP:63a12d3b46928bf6fece89f1a1a51579ab162d84 |
Federated Averaging as Expectation Maximization | 1 INTRODUCTION . Smart devices have become ubiquitous in today ’ s world and are generating large amounts of potentially sensitive data . Traditionally , such data is transmitted and stored in a central location for training machine learning models . Such methods rightly raise privacy concerns and we seek the means for... | Federated learning has emerged as a promising approach to training models at the edge devices. This paper makes an observation that most algorithms used within federated learning, including the popular FedAvg, could be cast as instances of EM methods. The paper then continues to propose FedSparse, a federated learning ... | SP:0a93f0ca52d6a7b39a82c3a9e3199255a4fa4c84 |
Federated Averaging as Expectation Maximization | 1 INTRODUCTION . Smart devices have become ubiquitous in today ’ s world and are generating large amounts of potentially sensitive data . Traditionally , such data is transmitted and stored in a central location for training machine learning models . Such methods rightly raise privacy concerns and we seek the means for... | The paper proposes to re-interpret federated averaging (FedAvg) as a version of the expectation-maximization (EM) algorithm under a particular probabilistic model. Further, the authors propose to use spike-and-slab sparsity inducing priors over the local model parameters to sparsify the learned models (the correspondin... | SP:0a93f0ca52d6a7b39a82c3a9e3199255a4fa4c84 |
Unsupervised Video Decomposition using Spatio-temporal Iterative Inference | 1 INTRODUCTION . Unsupervised representation learning , which has a long history dating back to Boltzman Machines ( Hinton & Sejnowski , 1986 ) and original works of Marr ( 1970 ) , has recently emerged as one of the important directions of research , carrying the newfound promise of alleviating the need for excessivel... | In this paper, the authors propose to better explicitly utilize the sequential information in the video to improve the performance of unsupervised scene decomposition in video. Concretely, 2D LSTM is used to combine the advantages of iterative inference and temporal information. By appropriately using the inferred resu... | SP:dea62d40d829155b4a45b0c13adcdbe1bb080cf6 |
Unsupervised Video Decomposition using Spatio-temporal Iterative Inference | 1 INTRODUCTION . Unsupervised representation learning , which has a long history dating back to Boltzman Machines ( Hinton & Sejnowski , 1986 ) and original works of Marr ( 1970 ) , has recently emerged as one of the important directions of research , carrying the newfound promise of alleviating the need for excessivel... | The authors extend previous work of Greff et al. on unsupervised, multi-object scene decomposition to incorporate temporal information. In particular, they apply the LSTM defined for each candidate object not only over inference steps, but also over time. This allows the model to capture temporal cues, such as object ... | SP:dea62d40d829155b4a45b0c13adcdbe1bb080cf6 |
Network Pruning That Matters: A Case Study on Retraining Variants | 1 INTRODUCTION . Training neural networks is an everyday task in the era of deep learning and artificial intelligence . Generally speaking , given data availability , large and cumbersome networks are often preferred as they have more capacity to exhibit good data generalization . In the literature , large networks are... | This paper analyzes the role of learning rate in re-training after pruning, building on previous findings that changing the learning rate schedule of re-training can result in higher accuracy than low-learning-rate fine-tuning. The paper proposes several learning rate schedules to compare, specifically a cyclic learnin... | SP:914f06bae289e10ae114cc43130751b8a8859b46 |
Network Pruning That Matters: A Case Study on Retraining Variants | 1 INTRODUCTION . Training neural networks is an everyday task in the era of deep learning and artificial intelligence . Generally speaking , given data availability , large and cumbersome networks are often preferred as they have more capacity to exhibit good data generalization . In the literature , large networks are... | The authors conducted a comprehensive set of experiments on choices of learning rate schedules for re-training/fine-tuning during iterative or after 1-shot pruning of deep convnets. Empirically, they reported that high learning rate (LR) is particularly helpful in recovering generalization performance of the resultant... | SP:914f06bae289e10ae114cc43130751b8a8859b46 |
Graph Pooling by Edge Cut | 1 INTRODUCTION . Convolution neural networks ( LeCun et al. , 1995 ) have been proven to be very efficient at learning meaningful patterns for many articificial intelligence tasks . They convey the ability to learn hierarchical informations in data with Euclidean grid-like structures such as images and textual data . C... | The paper proposes a novel pooling layer for graph neural networks. Pooling in GNNs amounts to merging nodes that are very similar through the layers. Specifically, the paper proposes to merge nodes whose edges have a high score according to the edge cuts. The edge score in practice is computed in each layer using an a... | SP:e2541b2195db1d6b0025113818f7d7a653473370 |
Graph Pooling by Edge Cut | 1 INTRODUCTION . Convolution neural networks ( LeCun et al. , 1995 ) have been proven to be very efficient at learning meaningful patterns for many articificial intelligence tasks . They convey the ability to learn hierarchical informations in data with Euclidean grid-like structures such as images and textual data . C... | This manuscript proposes a new pooling layer in Graph Neural Networks (GNN). By computing certain scores on edges which indicate the importance of edges in the process of information propagation, top r% edges are selected and a pooled graph is constructed by considering the connected components to be super nodes. The a... | SP:e2541b2195db1d6b0025113818f7d7a653473370 |
Hyperrealistic neural decoding: Reconstruction of face stimuli from fMRI measurements via the GAN latent space | 1 INTRODUCTION . In recent years , the field of neural decoding has been gaining more and more traction as advanced computational methods became increasingly available for application on neural data . This is a very welcome development in both neuroscience and neurotechnology since reading neural information will not o... | The paper proposes to reconstruct images of faces from fMRI measurements, using GANs. The authors collected a new dataset, showing static faces generated by a GAN model to human subjects, and recording their brain BOLD responses with MRI. Then, they learned a model to reconstruct the stimulus based on the brain respons... | SP:1fc0dee5fc3408ae717010debd346f9d2aff2c52 |
Hyperrealistic neural decoding: Reconstruction of face stimuli from fMRI measurements via the GAN latent space | 1 INTRODUCTION . In recent years , the field of neural decoding has been gaining more and more traction as advanced computational methods became increasingly available for application on neural data . This is a very welcome development in both neuroscience and neurotechnology since reading neural information will not o... | The manuscript entitled “Hyperrealistic neural decoding: Linear reconstruction of face stimuli from fMRI measurements via the GAN latent space” utilizes a GAN-based network structure for generating faces that are presented to the subjects during fMRI acquisition. The acquired fMRI signals are then used to predict laten... | SP:1fc0dee5fc3408ae717010debd346f9d2aff2c52 |
Adaptive Discretization for Continuous Control using Particle Filtering Policy Network | 1 INTRODUCTION . In the last few years , impressive results have been obtained by deep reinforcement learning ( DRL ) both on physical and simulated articulated agents for a wide range of motor tasks that involve learning controls in high-dimensional continuous action spaces ( Lillicrap et al. , 2015 ; Levine et al. , ... | This paper presents an approach to multimodal policies based on Gaussian mixtures. The policy is parameterized as a set of Gaussian distributions (with state-invariant mean and variance) weighted by state-dependent mixture weights, which are the output of a (softmaxed) network. The weighting network and the means and c... | SP:ce4b30f5da82d5d28aabe201cf6180b230ff4e26 |
Adaptive Discretization for Continuous Control using Particle Filtering Policy Network | 1 INTRODUCTION . In the last few years , impressive results have been obtained by deep reinforcement learning ( DRL ) both on physical and simulated articulated agents for a wide range of motor tasks that involve learning controls in high-dimensional continuous action spaces ( Lillicrap et al. , 2015 ; Levine et al. , ... | In the paper "Adaptive Discretization for Continuous Control using Particle Filtering Policy Network", the authors introduce a new way to discretise the action space of agent in RL settings by using a Particule Filtering approach. The main idea is that the learned policy will output the weight of each particle to defin... | SP:ce4b30f5da82d5d28aabe201cf6180b230ff4e26 |
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration | 1 INTRODUCTION . Data with noise or task-irrelevant information easily harm the training of a model ; for instance , the noisy-TV problem ( Burda et al. , 2019a ) is one of well-known such phenomena in reinforcement learning . If observations from the environment are modified to contain a TV screen , which changes its ... | The paper contributes a novel method, Drop-Bottleneck (DB), for discretely dropping input features that are irrelevant for predicting the target variable. Key idea is to instantiate the compression term of the information bottleneck framework with learned term that sets irrelevant feature dimensions to 0. To this end, ... | SP:eaeee8eeb85378f774e98259a43e7b8d794a3560 |
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration | 1 INTRODUCTION . Data with noise or task-irrelevant information easily harm the training of a model ; for instance , the noisy-TV problem ( Burda et al. , 2019a ) is one of well-known such phenomena in reinforcement learning . If observations from the environment are modified to contain a TV screen , which changes its ... | This paper proposes an information bottleneck method, Drop-Bottleneck, that allows the input to be compressed by dropping each input feature with probability p_i. The model then learns the drop probability vector p = [p_1, ... , p_n], where dropping "redundant" features will reduce the "compression penalty" term I(XZ).... | SP:eaeee8eeb85378f774e98259a43e7b8d794a3560 |
Cross-Modal Domain Adaptation for Reinforcement Learning | To overcome the unbearable reinforcement training of agents in the real-world , the sim-to-real approach , i.e. , training in simulators and adapting to target environments , is a promising direction . However , crafting a delicately simulator can also be difficult and costly . For example , to simulate vision-based ro... | The paper proposes a new approach for performing cross-modal domain adaptation, i.e. adapting a policy trained with inputs from modality A (eg low-dimensional environment state) to work with inputs from domain B (eg images). The main use case demonstrated in the paper is the adaptation of policies trained on states in ... | SP:3d06de343694b2f9db428428f68dee272e459486 |
Cross-Modal Domain Adaptation for Reinforcement Learning | To overcome the unbearable reinforcement training of agents in the real-world , the sim-to-real approach , i.e. , training in simulators and adapting to target environments , is a promising direction . However , crafting a delicately simulator can also be difficult and costly . For example , to simulate vision-based ro... | The authors pose a problem of learning a mapping when when a low-dimensional state simulation is given along with target image tragectories. The goal of the paper is to learn a mapping from image to state such that at test time the agent can directly use this mapping with a trained policy from the simulator to perform ... | SP:3d06de343694b2f9db428428f68dee272e459486 |
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... | This paper analyzes a learned loss function called TaylorGLO based on third-order Taylor expansion and its regularization properties. This approach is novel and interesting in that the loss function is also learned on data. The analysis of the TaylorGLO loss and another learned loss function Baikal loss near zero error... | SP:3021e8b74146a257b5befec014cd17d5d7bd3362 |
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