nils-herrmann/clean_scirepeval huggingface dataset:
Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs
Authors: Georg Kohl, Liwei Chen, Nils Thuerey

Simulations that produce three-dimensional data are ubiquitous in science, ranging from fluid flows to plasma physics. We propose a similarity model based on entropy, which allows for the creation of physically meaningful ground truth distances for the similarity assessment of scalar and vectorial data, produced from transport and motion-based simulations. Utilizing two data acquisition methods derived from this model, we create collections of fields from numerical PDE solvers and existing simulation data repositories, and highlight the importance of an appropriate data distribution for an effective training process. Furthermore, a multiscale CNN architecture that computes a volumetric similarity metric (VolSiM) is proposed. To the best of our knowledge this is the first learning method inherently designed to address the challenges arising for the similarity assessment of high-dimensional simulation data. Additionally, the tradeoff between a large batch size and an accurate correlation computation for correlation-based loss functions is investigated, and the metric's equivariance with respect to rotation and scale operations is analyzed. Finally, the robustness and generalization of VolSiM is evaluated on a large range of test data, as well as a particularly challenging turbulence case study, that is close to potential real-world applications.
Learning Latent Topology for Graph Matching
Authors: Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li

Graph matching (GM) has been traditionally modeled as a deterministic optimization problem characterized by an affinity matrix under pre-defined graph topology. Though there have been several attempts on learning more effective node-level affinity/representation for matching, they still heavily rely on the initial graph structure/topology which is typically obtained through heuristic ways (e.g. Delauney or $k$-nearest) and will not be adjusted during the learning process to adapt to problem-specific patterns. We argue that a standalone graph representation learning is insufficient for GM task, whereby a GM solver may favor some latent topology other than pre-defined one. Motivated by this hypothesis, we propose to learn latent graph topology in replacement of the fixed topology as input. To this end, we devise two types of latent graph generation procedures in deterministic and generative fashion, respectively. Particularly, the generative procedure emphasizes the across-graph consistency and thus can be viewed as a \textbf{co-generative} model. Our methods show superior performance over previous state-of-the-arts on several benchmarks, thus strongly supporting our hypothesis.
Safe Policy Learning for Continuous Control
Authors: Yinlam Chow, Ofir Nachum, Aleksandra Faust, Edgar Duenez-Guzman, Mohammad Ghavamzadeh

We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that keep the agent in desirable situations, both during training and at convergence. We formulate these problems as {\em constrained} Markov decision processes (CMDPs) and present safe policy optimization algorithms that are based on a Lyapunov approach to solve them. Our algorithms can use any standard policy gradient (PG) method, such as deep deterministic policy gradient (DDPG) or proximal policy optimization (PPO), to train a neural network policy, while guaranteeing near-constraint satisfaction for every policy update by projecting either the policy parameter or the selected action onto the set of feasible solutions induced by the state-dependent linearized Lyapunov constraints. Compared to the existing constrained PG algorithms, ours are more data efficient as they are able to utilize both on-policy and off-policy data. Moreover, our action-projection algorithm often leads to less conservative policy updates and allows for natural integration into an end-to-end PG training pipeline. We evaluate our algorithms and compare them with the state-of-the-art baselines on several simulated (MuJoCo) tasks, as well as a real-world robot obstacle-avoidance problem, demonstrating their effectiveness in terms of balancing performance and constraint satisfaction.
When Robustness Doesn’t Promote Robustness: Synthetic vs. Natural Distribution Shifts on ImageNet
Authors: Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt

We conduct a large experimental comparison of various robustness metrics for image classification. The main question of our study is to what extent current synthetic robustness interventions (lp-adversarial examples, noise corruptions, etc.) promote robustness under natural distribution shifts occurring in real data. To this end, we evaluate 147 ImageNet models under 199 different evaluation settings. We find that no current robustness intervention improves robustness on natural distribution shifts beyond a baseline given by standard models without a robustness intervention. The only exception is the use of larger training datasets, which provides a small increase in robustness on one natural distribution shift. Our results indicate that robustness improvements on real data may require new methodology and more evaluations on natural distribution shifts.
Inferring Dynamical Systems with Long-Range Dependencies through Line Attractor Regularization
Authors: Dominik Schmidt, Georgia Koppe, Max Beutelspacher, Daniel Durstewitz

Vanilla RNN with ReLU activation have a simple structure that is amenable to systematic dynamical systems analysis and interpretation, but they suffer from the exploding vs. vanishing gradients problem. Recent attempts to retain this simplicity while alleviating the gradient problem are based on proper initialization schemes or orthogonality/unitary constraints on the RNN’s recurrency matrix, which, however, comes with limitations to its expressive power with regards to dynamical systems phenomena like chaos or multi-stability. Here, we instead suggest a regularization scheme that pushes part of the RNN’s latent subspace toward a line attractor configuration that enables long short-term memory and arbitrarily slow time scales. We show that our approach excels on a number of benchmarks like the sequential MNIST or multiplication problems, and enables reconstruction of dynamical systems which harbor widely different time scales.
Reinforcement and Imitation Learning for Diverse Visuomotor Skills
Authors: Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, Nicolas Heess

We propose a general deep reinforcement learning method and apply it to robot manipulation tasks. Our approach leverages demonstration data to assist a reinforcement learning agent in learning to solve a wide range of tasks, mainly previously unsolved. We train visuomotor policies end-to-end to learn a direct mapping from RGB camera inputs to joint velocities. Our experiments indicate that our reinforcement and imitation approach can solve contact-rich robot manipulation tasks that neither the state-of-the-art reinforcement nor imitation learning method can solve alone. We also illustrate that these policies achieved zero-shot sim2real transfer by training with large visual and dynamics variations.
Adaptive Optimizers with Sparse Group Lasso
Authors: Yun Yue, Suo Tong, Zhen Zhang, Yongchao Liu, Chunyang Wen, Huanjun Bao, Jinjie Gu, Yixiang Mu

We develop a novel framework that adds the regularizers to a family of adaptive optimizers in deep learning, such as MOMENTUM, ADAGRAD, ADAM, AMSGRAD, ADAHESSIAN, and create a new class of optimizers, which are named GROUP MOMENTUM, GROUP ADAGRAD, GROUP ADAM, GROUP AMSGRAD and GROUP ADAHESSIAN, etc., accordingly. We establish theoretically proven convergence guarantees in the stochastic convex settings, based on primal-dual methods. We evaluate the regularized effect of our new optimizers on three large-scale real-world ad click datasets with state-of-the-art deep learning models. The experimental results reveal that compared with the original optimizers with the post-processing procedure which use the magnitude pruning method, the performance of the models can be significantly improved on the same sparsity level. Furthermore, in comparison to the cases without magnitude pruning, our methods can achieve extremely high sparsity with significantly better or highly competitive performance.
What Can Learned Intrinsic Rewards Capture?
Authors: Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh

Reinforcement learning agents can include different components, such as policies, value functions, state representations, and environment models. Any or all of these can be the loci of knowledge, i.e., structures where knowledge, whether given or learned, can be deposited and reused. Regardless of its composition, the objective of an agent is behave so as to maximise the sum of suitable scalar functions of state: the rewards. As far as the learning algorithm is concerned, these rewards are typically given and immutable. In this paper we instead consider the proposition that the reward function itself may be a good locus of knowledge. This is consistent with a common use, in the literature, of hand-designed intrinsic rewards to improve the learning dynamics of an agent. We adopt a multi-lifetime setting of the Optimal Rewards Framework, and investigate how meta-learning can be used to find good reward functions in a data-driven way. To this end, we propose to meta-learn an intrinsic reward function that allows agents to maximise their extrinsic rewards accumulated until the end of their lifetimes. This long-term lifetime objective allows our learned intrinsic reward to generate systematic multi-episode exploratory behaviour. Through proof-of-concept experiments, we elucidate interesting forms of knowledge that may be captured by a suitably trained intrinsic reward such as the usefulness of exploring uncertain states and rewards.
Transformation Autoregressive Networks
Authors: Junier Oliva, Avinava Dubey, Barnabás Póczos, Eric P. Xing, Jeff Schneider

The fundamental task of general density estimation has been of keen interest to machine learning. Recent advances in density estimation have either: a) proposed using a flexible model to estimate the conditional factors of the chain rule; or b) used flexible, non-linear transformations of variables of a simple base distribution. Instead, this work jointly leverages transformations of variables and autoregressive conditional models, and proposes novel methods for both. We provide a deeper understanding of our models, showing a considerable improvement with our methods through a comprehensive study over both real world and synthetic data. Moreover, we illustrate the use of our models in outlier detection and image modeling task.
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators
Authors: Yongseok Choi, Junyoung Park, Subin Yi, Dong-Yeon Cho

Although few-shot learning research has advanced rapidly with the help of meta-learning, its practical usefulness is still limited because most of the researches assumed that all meta-training and meta-testing examples came from a single domain. We propose a simple but effective way for few-shot classification in which a task distribution spans multiple domains including previously unseen ones during meta-training.
The key idea is to build a pool of embedding models which have their own metric spaces and to learn to select the best one for a particular task through multi-domain meta-learning. This simplifies task-specific adaptation over a complex task distribution as a simple selection problem rather than modifying the model with a number of parameters at meta-testing time. Inspired by common multi-task learning techniques, we let all models in the pool share a base network and add a separate modulator to each model to refine the base network in its own way. This architecture allows the pool to maintain representational diversity and each model to have domain-invariant representation as well. 
Experiments show that our selection scheme outperforms other few-shot classification algorithms when target tasks could come from many different domains. They also reveal that aggregating outputs from all constituent models is effective for tasks from unseen domains showing the effectiveness of our framework.
BUZz: BUffer Zones for defending  adversarial examples in image classification
Authors: Phuong Ha Nguyen*, Kaleel Mahmood*, Lam M. Nguyen, Thanh Nguyen, Marten van Dijk

We propose a novel defense against all existing gradient based adversarial attacks on deep neural networks for image classification problems. Our defense is based on a combination of deep neural networks and simple image transformations. While straight forward in implementation, this defense yields a unique security property which we term buffer zones. In this paper, we formalize the concept of buffer zones. We argue that our defense based on buffer zones is secure  against state-of-the-art black box attacks. We are able to achieve this security even when the adversary has access to the entire original training data set and unlimited query access to the defense. We verify our security claims through experimentation using FashionMNIST, CIFAR-10 and CIFAR-100. We demonstrate <10% attack success rate -- significantly lower than what other well-known defenses offer -- at only a price of a 15-20% drop in clean accuracy. By using a new intuitive metric we explain why this trade-off offers a significant improvement over prior work.
Luring of transferable adversarial perturbations in the black-box paradigm
Authors: Rémi Bernhard, Pierre-Alain Moëllic, Jean-Max Dutertre

The growing interest for adversarial examples, i.e. maliciously modified examples which fool a classifier, has resulted in many defenses intended to detect them, render them inoffensive or make the model more robust against them. In this paper, we pave the way towards a new approach to improve the robustness of a model against black-box transfer attacks. A removable additional neural network is included in the target model and is designed to induce the "luring effect", which tricks the adversary into choosing false directions to fool the target model. Training the additional model is achieved thanks to a loss function acting on the logits sequence order. Our deception-based method only needs to have access to the predictions of the target model and does not require a labeled data set. We explain the luring effect thanks to the notion of robust and non-robust useful features and perform experiments on MNIST, SVHN and CIFAR10 to characterize and evaluate this phenomenon. Additionally, we discuss two simple prediction schemes, and verify experimentally that our approach can be used as a defense to efficiently thwart an adversary using state-of-the-art attacks and allowed to perform large perturbations.
Neural Tangent Kernel Empowered Federated Learning
Authors: Kai Yue, Richeng Jin, Ryan Pilgrim, Chau-Wai Wong, Dror Baron, Huaiyu Dai

Federated learning (FL) is a privacy-preserving paradigm where multiple participants jointly solve a machine learning problem without sharing raw data. Unlike traditional distributed learning, a unique characteristic of FL is statistical heterogeneity, namely, data distributions across participants are different from each other. Meanwhile, recent advances in the interpretation of neural networks have seen a wide use of neural tangent kernel (NTK) for convergence and generalization analyses. In this paper, we propose a novel FL paradigm empowered by the NTK framework. The proposed paradigm addresses the challenge of statistical heterogeneity by transmitting update data that are more expressive than those of the traditional FL paradigms. Specifically, sample-wise Jacobian matrices, rather than model weights/gradients, are uploaded by participants. The server then constructs an empirical kernel matrix to update a global model without explicitly performing gradient descent. We further develop a variant with improved communication efficiency and enhanced privacy. Numerical results show that the proposed paradigm can achieve the same accuracy while reducing the number of communication rounds by an order of magnitude compared to federated averaging.
Delayed Geometric Discounts: An alternative criterion for Reinforcement Learning
Authors: Firas Jarboui, Ahmed Akakzia

The endeavor of artificial intelligence (AI) is to design autonomous agents capable of achieving complex tasks. Namely, reinforcement learning (RL) proposes a theoretical background to learn optimal behaviors. In practice, RL algorithms rely on geometric discounts to evaluate this optimality. Unfortunately, this does not cover decision processes where future returns are not exponentially less valuable.
Depending on the problem, this limitation induces sample-inefficiency (as feed-backs are exponentially decayed) and requires additional curricula/exploration mechanisms (to deal with sparse, deceptive or adversarial rewards).
In this paper, we tackle these issues by generalizing the discounted problem formulation with a family of delayed objective functions. We investigate the underlying RL problem to derive: 1) the optimal stationary solution and 2) an approximation of the optimal non-stationary control. The devised algorithms solved hard exploration problems on tabular environment and improved sample-efficiency on classic simulated robotics benchmarks.
STABILITY AND CONVERGENCE THEORY FOR LEARNING RESNET: A FULL CHARACTERIZATION
Authors: Huishuai Zhang, Da Yu, Mingyang Yi, Wei Chen, Tie-yan Liu

ResNet structure has achieved great success since its debut. In this paper, we study the stability of learning ResNet. Specifically, we consider the ResNet block $h_l = \phi(h_{l-1}+\tau\cdot g(h_{l-1}))$ where $\phi(\cdot)$ is ReLU activation and $\tau$ is a scalar. We show that for standard initialization used in practice, $\tau =1/\Omega(\sqrt{L})$ is a sharp value in characterizing the stability of forward/backward process of ResNet, where $L$ is the number of residual blocks. Specifically, stability is guaranteed for $\tau\le 1/\Omega(\sqrt{L})$ while conversely forward process explodes when $\tau>L^{-\frac{1}{2}+c}$ for a positive constant $c$.  Moreover,  if ResNet is properly over-parameterized, we show for $\tau \le 1/\tilde{\Omega}(\sqrt{L})$ gradient descent is guaranteed to find the global minima \footnote{We use $\tilde{\Omega}(\cdot)$ to hide logarithmic factor.}, which significantly enlarges the range of $\tau\le 1/\tilde{\Omega}(L)$ that admits  global convergence  in previous work. We also demonstrate that the over-parameterization requirement of ResNet only weakly depends on the depth, which corroborates the advantage of ResNet over vanilla feedforward network. Empirically, with $\tau\le1/\sqrt{L}$, deep ResNet can be easily trained even without normalization layer. Moreover, adding $\tau=1/\sqrt{L}$ can also improve the performance of ResNet with normalization layer.
Visualizing the Loss Landscape of Neural Nets
Authors: Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein

Neural network training relies on our ability to find ````````"good" minimizers of highly non-convex loss functions. It is well known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and well-chosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effect on the underlying loss landscape, is not well understood.

In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple ``"filter normalization" method that helps us visualize loss function curvature, and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture effects the loss landscape, and how training parameters affect the shape of minimizers.
Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior
Authors: Charles H. Martin, Michael W. Mahoney

We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks.  The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks.  Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped).  Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.
Where Off-Policy Deep Reinforcement Learning Fails
Authors: Scott Fujimoto, David Meger, Doina Precup

This work examines batch reinforcement learning--the task of maximally exploiting a given batch of off-policy data, without further data collection. We demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are only capable of learning with data correlated to their current policy, making them ineffective for most off-policy applications. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space to force the agent towards behaving on-policy with respect to a subset of the given data. We extend this notion to deep reinforcement learning, and to the best of our knowledge, present the first continuous control deep reinforcement learning algorithm which can learn effectively from uncorrelated off-policy data.
Monotonic neural network: combining deep learning with domain knowledge for chiller plants energy optimization
Authors: Fanhe Ma, Faen Zhang, Shenglan Ben, Shuxin Qin, pengcheng Zhou, Changsheng Zhou, Fengyi Xu

In this paper, we are interested in building a domain knowledge based deep learning framework to solve the chiller plants energy optimization problems. Compared to the hotspot applications of deep learning (e.g. image classification and NLP), it is difficult to collect enormous data for deep network training in real-world physical systems. Most existing methods reduce the complex systems into linear model to facilitate the training on small samples. To tackle the small sample size problem, this paper considers domain knowledge in the structure and loss design of deep network to build a nonlinear model with lower redundancy function space. Specifically, the energy consumption estimation of most chillers can be physically viewed as an input-output monotonic problem. Thus, we can design a Neural Network with monotonic constraints to mimic the physical behavior of the system. We verify the proposed method in a cooling system of a data center, experimental results show the superiority of our framework in energy optimization compared to the existing ones.