nils-herrmann/clean_scirepeval huggingface dataset:
Variational Recurrent Adversarial Deep Domain Adaptation
Authors: Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, Yan Liu

We study the problem of learning domain invariant representations for time series data while transferring the complex temporal latent dependencies between the domains. Our model termed as Variational Recurrent Adversarial Deep Domain Adaptation (VRADA) is built atop a variational recurrent neural network (VRNN) and trains adversarially to capture complex temporal relationships that are domain-invariant. This is (as far as we know) the first to capture and transfer temporal latent dependencies in multivariate time-series data. Through experiments on real-world multivariate healthcare time-series datasets, we empirically demonstrate that learning temporal dependencies helps our model's ability to create domain-invariant representations, allowing our model to outperform current state-of-the-art deep domain adaptation approaches.
Detecting malicious PDF using CNN
Authors: Raphael Fettaya, Yishay Mansour

Malicious PDF files represent one of the biggest threats to computer security. To
detect them, significant research has been done using handwritten signatures or
machine learning based on manual feature extraction. Those approaches are both
time-consuming, requires significant prior knowledge and the list of features has
to be updated with each newly discovered vulnerability. In this work, we propose
a novel algorithm that uses a Convolutional Neural Network (CNN) on the byte
level of the file, without any handcrafted features. We show, using a data set
of 130000 files, that our approach maintains a high detection rate (96%) of PDF
malware and even detects new malicious files, still undetected by most antiviruses.
Using automatically generated features from our CNN network, and applying a
clustering algorithm, we also obtain high similarity between the antiviruses’ labels
and the resulting clusters.
What's new? Summarizing Contributions in Scientific Literature
Authors: Hiroaki Hayashi, Wojciech Maciej Kryscinski, Bryan McCann, Nazneen Rajani, Caiming Xiong

With thousands of academic articles shared on a daily basis, it has become increasingly difficult to keep up with the latest scientific findings. To overcome this problem, we introduce a new task of $\textit{disentangled paper summarization}$, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles. For this purpose, we extend the S2ORC corpus of academic articles, which spans a diverse set of domains ranging from economics to psychology, by adding disentangled "contribution" and "context" reference labels. Together with the dataset, we introduce and analyze three baseline approaches: 1) a unified model controlled by input code prefixes, 2) a model with separate generation heads specialized in generating the disentangled outputs, and 3) a training strategy that guides the model using additional supervision coming from inbound and outbound citations. We also propose a comprehensive automatic evaluation protocol which reports the $\textit{relevance}$, $\textit{novelty}$, and $\textit{disentanglement}$ of generated outputs. Through a human study involving expert annotators, we show that in 79%, of cases our new task is considered more helpful than traditional scientific paper summarization.
Learning an Embedding Space for Transferable Robot Skills
Authors: Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin Riedmiller

We present a method for reinforcement learning of closely related skills that are parameterized via a skill embedding space. We learn such skills by taking advantage of latent variables and exploiting a connection between reinforcement learning and variational inference. The main contribution of our work is an entropy-regularized policy gradient formulation for hierarchical policies, and an associated, data-efficient and robust off-policy gradient algorithm based on stochastic value gradients. We demonstrate the effectiveness of our method on several simulated robotic manipulation tasks. We find that our method allows for discovery of multiple solutions and is capable of learning the minimum number of distinct skills that are necessary to solve a given set of tasks. In addition, our results indicate that the hereby proposed technique can interpolate and/or sequence previously learned skills in order to accomplish more complex tasks, even in the presence of sparse rewards.
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Authors: Shauharda Khadka, Somdeb Majumdar, Santiago Miret, Stephen McAleer, Kagan Tumer

Many cooperative multiagent reinforcement learning environments provide agents with a sparse team-based reward as well as a dense agent-specific reward that incentivizes learning basic skills. Training policies solely on the team-based reward is often difficult due to its sparsity. Also, relying solely on the agent-specific reward is sub-optimal because it usually does not capture the team coordination objective. A common approach is to use reward shaping to construct a proxy reward by combining the individual rewards. However, this requires manual tuning for each environment. We introduce Multiagent Evolutionary Reinforcement Learning (MERL), a split-level training platform that handles the two objectives separately through two optimization processes. An evolutionary algorithm maximizes the sparse team-based objective through neuroevolution on a population of teams. Concurrently, a gradient-based optimizer trains policies to only maximize the dense agent-specific rewards. The gradient-based policies are periodically added to the evolutionary population as a way of information transfer between the two optimization processes. This enables the evolutionary algorithm to use skills learned via the agent-specific rewards toward optimizing the global objective. Results demonstrate that MERL significantly outperforms state-of-the-art methods such as MADDPG on a number of difficult coordination benchmarks.
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Authors: Simon S. Du, Xiyu Zhai, Barnabas Poczos, Aarti Singh

One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function.

Our analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods.
What Preserves the Emergence of Language?
Authors: Ziluo Ding, Tiejun Huang, Zongqing Lu

The emergence of language is a mystery. One dominant theory is that cooperation boosts language to emerge. However, as a means of giving out information, language seems not to be an evolutionarily stable strategy. To ensure the survival advantage of many competitors, animals are selfish in nature. From the perspective of Darwinian, if an individual can obtain a higher benefit by deceiving the other party, why not deceive? For those who are cheated, once bitten and twice shy, cooperation will no longer be a good option. As a result, motivation for communication, as well as the emergence of language would perish. Then, what preserves the emergence of language? We aim to answer this question in a brand new framework of agent community, reinforcement learning, and natural selection. Empirically, we reveal that lying indeed dispels cooperation. Even with individual resistance to lying behaviors, liars can easily defeat truth tellers and survive during natural selection. However, social resistance eventually constrains lying and makes the emergence of language possible.
Noise or Signal: The Role of Image Backgrounds in Object Recognition
Authors: Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry

We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misclassify images even in the presence of correctly classified foregrounds--up to 88% of the time with adversarially chosen backgrounds, and (c) more accurate models tend to depend on backgrounds less. Our analysis of backgrounds brings us closer to understanding which correlations machine learning models use, and how they determine models' out of distribution performance.
Graph Neural Networks For Multi-Image Matching
Authors: Stephen Phillips, Kostas Daniilidis

In geometric computer vision applications, multi-image feature matching gives more accurate and robust solutions compared to simple two-image matching. In this work, we formulate multi-image matching as a graph embedding problem, then use a Graph Neural Network to learn an appropriate embedding function for aligning image features. We use cycle consistency to train our network in an unsupervised fashion, since ground truth correspondence can be difficult or expensive to acquire. Geometric consistency losses are added to aid training, though unlike optimization based methods no geometric information is necessary at inference time. To the best of our knowledge, no other works have used graph neural networks for multi-image feature matching. Our experiments show that our method is competitive with other optimization based approaches.
Label super-resolution networks
Authors: Kolya Malkin, Caleb Robinson, Le Hou, Rachel Soobitsky, Jacob Czawlytko, Dimitris Samaras, Joel Saltz, Lucas Joppa, Nebojsa Jojic

We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels. This method involves a novel loss function that minimizes the distance between a distribution determined by a set of model outputs and the corresponding distribution given by low-resolution labels over the same set of outputs. This setup does not require that the high-resolution classes match the low-resolution classes and can be used in high-resolution semantic segmentation tasks where high-resolution labeled data is not available. Furthermore, our proposed method is able to utilize both data with low-resolution labels and any available high-resolution labels, which we show improves performance compared to a network trained only with the same amount of high-resolution data.
We test our proposed algorithm in a challenging land cover mapping task to super-resolve labels at a 30m resolution to a separate set of labels at a 1m resolution. We compare our algorithm with models that are trained on high-resolution data and show that 1) we can achieve similar performance using only low-resolution data; and 2) we can achieve better performance when we incorporate a small amount of high-resolution data in our training. We also test our approach on a medical imaging problem, resolving low-resolution probability maps into high-resolution segmentation of lymphocytes with accuracy equal to that of fully supervised models.
Learning Representations for Faster Similarity Search
Authors: Ludwig Schmidt, Kunal Talwar

In high dimensions, the performance of nearest neighbor algorithms depends crucially on structure in the data.
While traditional nearest neighbor datasets consisted mostly of hand-crafted feature vectors, an increasing number of datasets comes from representations learned with neural networks.
We study the interaction between nearest neighbor algorithms and neural networks in more detail.
We find that the network architecture can significantly influence the efficacy of nearest neighbor algorithms even when the classification accuracy is unchanged.
Based on our experiments, we propose a number of training modifications that lead to significantly better datasets for nearest neighbor algorithms.
Our modifications lead to learned representations that can accelerate nearest neighbor queries by 5x.
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bitwise Regularization
Authors: Jung Hyun Lee, Jihun Yun, Sung Ju Hwang, Eunho Yang

Network quantization, which aims to reduce the bit-lengths of the network weights and activations, has emerged as one of the key ingredients to reduce the size of neural networks for their deployments to resource-limited devices. In order to overcome the nature of transforming continuous activations and weights to discrete ones, recent study called Relaxed Quantization (RQ) [Louizos et al. 2019] successfully employ the popular Gumbel-Softmax that allows this transformation with efficient gradient-based optimization. However, RQ with this Gumbel-Softmax relaxation still suffers from bias-variance trade-off depending on the temperature parameter of Gumbel-Softmax. To resolve the issue, we propose a novel method, Semi-Relaxed Quantization (SRQ) that uses multi-class straight-through estimator to effectively reduce the bias and variance, along with a new regularization technique, DropBits that replaces dropout regularization to randomly drop the bits instead of neurons to further reduce the bias of the multi-class straight-through estimator in SRQ. As a natural extension of DropBits, we further introduce the way of learning heterogeneous quantization levels to find proper bit-length for each layer using DropBits. We experimentally validate our method on various benchmark datasets and network architectures, and also support the quantized lottery ticket hypothesis: learning heterogeneous quantization levels outperforms the case using the same but fixed quantization levels from scratch.
Representations of Computer Programs in the Human Brain
Authors: Shashank Srikant, Benjamin Lipkin, Anna A Ivanova, Evelina Fedorenko, Una-May O'Reilly

We present the first study relating representations of computer programs generated by unsupervised machine learning (ML) models and representations of computer programs in the human brain. We analyze recordings---brain representations---from functional magnetic resonance imaging (fMRI) studies of people comprehending Python code. We discover brain representations, in different and specific regions of the brain, that encode static and dynamic properties of code such as abstract syntax tree (AST)-related information and runtime information. We also map brain representations to representations of a suite of ML models that vary in their complexity. We find that the Multiple Demand system, a system of brain regions previously shown to respond to code, contains information about multiple specific code properties, as well as machine learned representations of code. We make all the corresponding code, data, and analysis publicly available.
Sensory Resilience based on Synesthesia
Authors: Eric Platon, Tom Sonoda

Situated cognition depends on accessing environmental state through sensors. Engineering and cost constraints usually lead to limited “pathways” where, for example, a vision sub-system only includes a camera and the software to deal with it. This traditional and rational design style entails any hardware defect on the pathway causes the system to grind to a halt until repair. We propose a “sensoriplexer” as drop-in neural component architecture to address this issue, under the common scenario of multiple sensors availability. This component architecture learns to mix and relate pathways, such that an agent facing failure in a sensory sub-system can degrade gracefully and coherently by relying on its other sub- systems. The architecture is inspired by the concept of synesthesia, and relies on statistical coupling between sensor signals. We show the benefit and limitation of the architecture on a simple shape recognition and a more complex emotion recognition scenarios.
Ensemble-based Adversarial Defense Using Diversified Distance Mapping
Authors: Ehsan Kazemi, Mohamed E. Hussein, Wael AbdAlmgaeed

We propose an ensemble-based defense against adversarial examples using distance map layers (DMLs). Similar to fully connected layers, DMLs can be used to output logits for a multi-class classification model. We show in this paper how DMLs can be deployed to prevent transferability  of attacks across ensemble members by adapting pairwise (almost) orthogonal covariance matrices. We also illustrate how DMLs provide an efficient way to regularize the Lipschitz constant of the ensemble's member models, which further boosts the resulting robustness. Through empirical evaluations across multiple datasets and attack models, we demonstrate that the ensembles based on DMLs can achieve high benign accuracy while exhibiting robustness against adversarial attacks using multiple white-box techniques along with AutoAttack.
Factored Action Spaces in Deep Reinforcement Learning
Authors: Thomas PIERROT, Valentin Macé, Jean-Baptiste Sevestre, Louis Monier, Alexandre Laterre, Nicolas Perrin, Karim Beguir, Olivier Sigaud

Very large action spaces constitute a critical challenge for deep Reinforcement Learning (RL) algorithms. An existing approach consists in splitting the action space into smaller components and choosing either independently or sequentially actions in each dimension. This approach led to astonishing results for the StarCraft and Dota 2 games, however it remains underexploited and understudied. In this paper, we name this approach Factored Actions Reinforcement Learning (FARL) and study both its theoretical impact and practical use. Notably, we provide a theoretical analysis of FARL on the Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC) algorithms and evaluate these agents in different classes of problems. We show that FARL is a very versatile and efficient approach to combinatorial and continuous control problems.
Egocentric Spatial Memory Network
Authors: Mengmi Zhang, Keng Teck Ma, Joo Hwee Lim, Shih-Cheng Yen, Qi Zhao, Jiashi Feng

Inspired by neurophysiological discoveries of navigation cells in the mammalian
brain, we introduce the first deep neural network architecture for modeling Egocentric
Spatial Memory (ESM). It learns to estimate the pose of the agent and
progressively construct top-down 2D global maps from egocentric views in a spatially
extended environment. During the exploration, our proposed ESM network
model updates belief of the global map based on local observations using a recurrent
neural network. It also augments the local mapping with a novel external
memory to encode and store latent representations of the visited places based on
their corresponding locations in the egocentric coordinate. This enables the agents
to perform loop closure and mapping correction. This work contributes in the
following aspects: first, our proposed ESM network provides an accurate mapping
ability which is vitally important for embodied agents to navigate to goal locations.
In the experiments, we demonstrate the functionalities of the ESM network in
random walks in complicated 3D mazes by comparing with several competitive
baselines and state-of-the-art Simultaneous Localization and Mapping (SLAM)
algorithms. Secondly, we faithfully hypothesize the functionality and the working
mechanism of navigation cells in the brain. Comprehensive analysis of our model
suggests the essential role of individual modules in our proposed architecture and
demonstrates efficiency of communications among these modules. We hope this
work would advance research in the collaboration and communications over both
fields of computer science and computational neuroscience.
Secure Network Release with Link Privacy
Authors: Carl Yang, Haonan Wang, Ke ZHANG, Lichao Sun

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sensitive, we aim to seek effective approaches to release utility-preserved yet privacy-protected structured data. In this paper, we leverage the differential privacy (DP) framework, to formulate and enforce rigorous privacy constraints on deep graph generation models, with a focus on edge-DP to guarantee individual link privacy. In particular, we enforce edge-DP by injecting Gaussian noise to the gradients of a link reconstruction based graph generation model, and ensure data utility by improving structure learning with structure-oriented graph comparison. Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate networks with effectively preserved global structure and rigorously protected individual link privacy.
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Authors: Yurong You, Katie Z Luo, Xiangyu Chen, Junan Chen, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, Kilian Q Weinberger

Self-driving cars must detect vehicles, pedestrians, and other trafﬁc participants accurately to operate safely. Small, far-away, or highly occluded objects are particularly challenging because there is limited information in the LiDAR point clouds for detecting them. To address this challenge, we leverage valuable information from the past: in particular, data collected in past traversals of the same scene. We posit that these past data, which are typically discarded, provide rich contextual information for disambiguating the above-mentioned challenging cases. To this end, we propose a novel end-to-end trainable Hindsight framework to extract this contextual information from past traversals and store it in an easy-to-query data structure, which can then be leveraged to aid future 3D object detection of the same scene. We show that this framework is compatible with most modern 3D detection architectures and can substantially improve their average precision on multiple autonomous driving datasets, most notably by more than 300% on the challenging cases. Our code is available at https://github.com/YurongYou/Hindsight.
LASSO: Latent Sub-spaces Orientation for Domain Generalization
Authors: Long Tung Vuong, Trung Quoc Phung, Toan Tran, Anh Tuan Tran, Dinh Phung, Trung Le

To achieve a satisfactory generalization performance on prediction tasks in an unseen domain, existing domain generalization (DG) approaches often rely on the strict assumption of fixed domain-invariant features and common hypotheses learned from a set of training domains. While it is a natural and important premise to ground generalization capacity on the target domain, we argue that this assumption could be overly strict and sub-optimal. It is particularly evident when source domains share little information or the target domains leverages information from selective source domains in a compositional way instead of relying on a unique invariant hypothesis across all source domains. Unlike most existing approaches, instead of constructing a single hypothesis shared among domains, we propose a LAtent Sub-Space Orientation (LASSO) method that explores diverse latent sub-spaces and learning individual hypotheses on those sub-spaces. Moreover, in LASSO, since the latent sub-spaces are formed by the label-informative features captured in source domains, they allow us to project target examples onto appropriate sub-spaces, while preserving crucial label-informative features for the label prediction. Finally, we empirically evaluate our method on several well-known DG benchmarks, where it achieves state-of-the-art results.