nils-herrmann/clean_scirepeval huggingface dataset:
Understanding and Exploiting the Low-Rank Structure of Deep Networks
Authors: Craig Bakker, Michael J. Henry, Nathan O. Hodas

Training methods for deep networks are primarily variants on stochastic gradient descent.  Techniques that use (approximate) second-order information are rarely used because of the computational cost and noise associated with those approaches in deep learning contexts.  However, in this paper, we show how feedforward deep networks exhibit a low-rank derivative structure.  This low-rank structure makes it possible to use second-order information without needing approximations and without incurring a significantly greater computational cost than gradient descent.  To demonstrate this capability, we implement Cubic Regularization (CR) on a feedforward deep network with stochastic gradient descent and two of its variants.  There, we use CR to calculate learning rates on a per-iteration basis while training on the MNIST and CIFAR-10 datasets.  CR proved particularly successful in escaping plateau regions of the objective function.  We also found that this approach requires less problem-specific information (e.g. an optimal initial learning rate) than other first-order methods in order to perform well.
Learning-Augmented Sketches for Hessians
Authors: Yi Li, Honghao Lin, David Woodruff

We study learning-based sketching for Hessians, which is known to provide considerable speedups to second order optimization. A number of works have shown how to sketch or subsample the Hessian to speed up each iteration, but such sketches are usually specific to the matrix at hand, rather than being learned from a distribution. We extend such schemes to learned sketches, where we learn different potentially different sketches for the different iterations, and show empirically that learned sketches, compared with their "non-learned" counterparts, improve the approximation accuracy for a large number of important problems, including LASSO, SVM, and matrix estimation with nuclear norm constraints.
Combining Differential Privacy and Byzantine Resilience in Distributed SGD
Authors: Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Sébastien Rouault, John Stephan

Privacy and Byzantine resilience (BR) are two crucial requirements of modern-day distributed machine learning. The two concepts have been extensively studied individually but the question of how to combine them effectively remains unanswered. This paper contributes to addressing this question by studying the extent to which the distributed SGD algorithm, in the standard parameter-server architecture, can learn an accurate model despite (a) a fraction of the workers being malicious (Byzantine), and (b) the other fraction, whilst being honest, providing noisy information to the server to ensure differential privacy (DP). We first observe that the integration of standard practices in DP and BR is not straightforward. In fact, we show that many existing results on the convergence of distributed SGD under Byzantine faults, especially those relying on $(\alpha,f)$-Byzantine resilience, are rendered invalid when honest workers enforce DP. To circumvent this shortcoming, we revisit the theory of $(\alpha,f)$-BR to obtain an approximate convergence guarantee. Our analysis provides key insights on how to improve this guarantee through hyperparameter optimization. Essentially, our theoretical and empirical results show that (1) an imprudent combination of standard approaches to DP and BR might be fruitless, but (2) by carefully re-tuning the learning algorithm, we can obtain reasonable learning accuracy while simultaneously guaranteeing DP and BR.
Second-Order Unsupervised Feature Selection via Knowledge Contrastive Distillation
Authors: Han Yue, Jundong Li, Hongfu Liu

Unsupervised feature selection aims to select a subset from the original features that are most useful for the downstream tasks without external guidance information. While most unsupervised feature selection methods focus on ranking features based on the intrinsic properties of data, they do not pay much attention to the relationships between features, which often leads to redundancy among the selected features. In this paper, we propose a two-stage Second-Order unsupervised Feature selection via knowledge contrastive disTillation (SOFT) model that incorporates the second-order covariance matrix with the first-order data matrix for unsupervised feature selection. In the first stage, we learn a sparse attention matrix that can represent second-order relations between features. In the second stage, we build a relational graph based on the learned attention matrix and perform graph segmentation for feature selection. Experimental results on 12 public datasets show that SOFT outperforms classical and recent state-of-the-art methods, which demonstrates the effectiveness of our proposed method.
Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness
Authors: Xiao Yang, Yinpeng Dong, Wenzhao Xiang, Tianyu Pang, Hang Su, Jun Zhu

The vulnerability of deep neural networks to adversarial examples has motivated an increasing number of defense strategies for promoting model robustness. However, the progress is usually hampered by insufficient robustness evaluations. As the de facto standard to evaluate adversarial robustness, adversarial attacks typically solve an optimization problem of crafting adversarial examples with an iterative process. In this work, we propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the optimizer in adversarial attacks parameterized by a recurrent neural network, which is trained over a class of data samples and defenses to produce effective update directions during adversarial example generation. Furthermore, we develop a model-agnostic training algorithm to improve the generalization ability of the learned optimizer when attacking unseen defenses. Our approach can be flexibly incorporated with various attacks and consistently improves the performance with little extra computational cost. Extensive experiments demonstrate the effectiveness of the learned attacks by MAMA compared to the state-of-the-art attacks on different defenses, leading to a more reliable evaluation of adversarial robustness.
Speeding up Deep Learning Training by Sharing Weights and Then Unsharing
Authors: Shuo Yang, Le Hou, Xiaodan Song, qiang liu, Denny Zhou

It has been widely observed that increasing deep learning model sizes often leads to significant performance improvements on a variety of natural language processing and computer vision tasks. In the meantime, however, computational costs and training time would dramatically increase when models get larger. In this paper, we propose a simple approach to speed up training for a particular kind of deep networks which contain repeated structures,  such as the transformer module. In our method, we first train such a deep network with the weights shared across all the repeated layers till some point. We then stop weight sharing and continue training until convergence. The untying point is automatically determined by monitoring gradient statistics. Our adaptive untying criterion is obtained from a theoretic analysis over deep linear networks.  Empirical results show that our method is able to reduce the training time of BERT  by 50%.
Conditional Expectation based Value Decomposition for Scalable On-Demand Ride Pooling
Authors: Avinandan Bose, Pradeep Varakantham

Owing to the benefits for customers (lower prices), drivers (higher revenues), aggregation companies (higher revenues) and the environment (fewer vehicles), on-demand ride pooling (e.g., Uber pool, Grab Share) has become quite popular. The significant computational complexity of matching vehicles to combinations of requests has meant that traditional ride pooling approaches are myopic in that they do not consider the impact of current matches on future value for vehicles/drivers.

Recently, Neural Approximate Dynamic Programming (NeurADP) has employed value decomposition with Approximate Dynamic Programming (ADP) to outperform leading approaches by considering the impact of an individual agent's (vehicle) chosen actions on the future value of that agent. However, in order to ensure scalability and facilitate city-scale ride pooling, NeurADP  completely ignores the impact of other agents actions on individual agent/vehicle value. As demonstrated in our experimental results, ignoring the impact of other agents actions on individual value can have a significant impact on the overall performance when there is increased competition among vehicles for demand. Our key contribution is a novel mechanism based on computing conditional expectations through joint conditional probabilities for capturing dependencies on other agents actions without increasing the complexity of training or decision making. We show that our new approach, Conditional Expectation based Value Decomposition (CEVD) outperforms NeurADP by up to 9.76$\% $in terms of overall requests served, which is a significant improvement on a city wide benchmark taxi dataset.
Recursive Construction of Stable Assemblies of Recurrent Neural Networks
Authors: Leo Kozachkov, Michaela M Ennis, Jean-Jacques Slotine

Advanced applications of modern machine learning will likely involve combinations of trained networks, as are already used in spectacular systems such as DeepMind's AlphaGo. Recursively building such combinations in an effective and stable fashion while also allowing for continual refinement of the individual networks - as nature does for biological networks - will require new analysis tools. This paper takes a step in this direction by establishing contraction properties of broad classes of nonlinear recurrent networks and neural ODEs, and showing how these quantified properties allow in turn to recursively construct stable networks of networks in a systematic fashion. The results can also be used to stably combine recurrent networks and physical systems with quantified contraction properties. Similarly, they may be applied to modular computational models of cognition. We perform experiments with these combined networks on benchmark sequential tasks (e.g permuted sequential MNIST) to demonstrate their capacity for processing information across a long timescale in a provably stable manner.
Understanding the Generalization Gap in Visual Reinforcement Learning
Authors: Anurag Ajay, Ge Yang, Ofir Nachum, Pulkit Agrawal

Deep Reinforcement Learning (RL) agents have achieved superhuman performance on several video game suites. However, unlike humans, the trained policies fail to transfer between related games or even between different levels of the same game. Recent works have attempted to reduce this generalization gap using ideas such as data augmentation and learning domain invariant features. However, the transfer performance still remains unsatisfactory. In this work, we use procedurally generated video games to empirically investigate several hypotheses to explain the lack of transfer. We also show that simple auxiliary tasks can improve the generalization of policies. Contrary to the belief that policy adaptation to new levels requires full policy finetuning, we find that visual features transfer across levels, and only the parameters, that use these visual features to predict actions, require finetuning. Finally, to inform fruitful avenues for future research, we construct simple oracle methods that close the generalization gap.
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
Authors: Lior Fox, Leshem Choshen, Yonatan Loewenstein

Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a major limitation of counters is their locality. While there are a few model-based solutions to this shortcoming, a model-free approach is still missing.
We propose $E$-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories. We compare our approach to commonly used RL techniques, and show that using $E$-values improves learning and performance over traditional counters. We also show how our method can be implemented with function approximation to efficiently learn continuous MDPs. We demonstrate this by showing that our approach surpasses state of the art performance in the Freeway Atari 2600 game.
DynCNN: An Effective Dynamic Architecture on Convolutional Neural Network for Surveillance Videos
Authors: De-Qin Gao, Ping-Chen Tsai, Shanq-Jang Ruan

The large-scale surveillance video analysis becomes important as the development of intelligent city. The heavy computation resources neccessary for state-of-the-art deep learning model makes the real-time processing hard to be implemented. This paper exploits the characteristic of high scene similarity generally existing in surveillance videos and proposes dynamic convolution reusing the previous feature map to reduce the computation amount. We tested the proposed method on 45 surveillance videos with various scenes. The experimental results show that dynamic convolution can reduce up to 75.7% of FLOPs while preserving the precision within 0.7% mAP. Furthermore, the dynamic convolution can enhance the processing time up to 2.2 times.
Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types
Authors: Shentong Mo, Xi Fu, Chenyang Hong, Yizhen Chen, Yuxuan Zheng, Xiangru Tang, Yanyan Lan, Zhiqiang Shen, Eric Xing

In the genome biology research, regulatory genome modeling is an important topic for many regulatory downstream tasks, such as promoter classification, transaction factor binding sites prediction. The core problem is to model how regulatory elements interact with each other and its variability across different cell types. However, current deep learning methods often focus on modeling genome sequences of a fixed set of cell types and do not account for the interaction between multiple regulatory elements, making them only perform well on the cell types in the training set and lack the generalizability required in biological applications. In this work, we propose a simple yet effective approach for pre-training genome data in a multi-modal and self-supervised manner, which we call $\textbf{\texttt{GeneBERT}}$. Specifically, we simultaneously take the 1d sequence of genome data and a 2d matrix of (transcription factors × regions) as the input, where three pre-training tasks are proposed to improve the robustness and generalizability of our model. We pre-train our model on the ATAC-seq dataset with 17 million genome sequences. We evaluate our GeneBERT on regulatory downstream tasks across different cell types, including promoter classification, transaction factor binding sites prediction, disease risk estimation, and splicing sites prediction. Extensive experiments demonstrate the effectiveness of multi-modal and self-supervised pre-training for large-scale regulatory genomics data.
$\ell_1$ Adversarial Robustness Certificates: a Randomized Smoothing Approach
Authors: Jiaye Teng, Guang-He Lee, Yang Yuan

Robustness is an important property to guarantee the security of machine learning models. It has recently been demonstrated that strong robustness certificates can be obtained on ensemble classifiers generated by input randomization. However, tight robustness certificates are only known for symmetric norms including $\ell_0$ and $\ell_2$, while for asymmetric norms like $\ell_1$, the existing techniques do not apply. By converting the likelihood ratio into a one-dimensional mixed random variable, we derive the first tight $\ell_1$ robustness certificate under isotropic Laplace distributions. Empirically, the deep networks smoothed by Laplace distributions yield the state-of-the-art certified robustness in $\ell_1$ norm on CIFAR-10 and ImageNet.
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
Authors: Kangjie Chen, Yuxian Meng, Xiaofei Sun, Shangwei Guo, Tianwei Zhang, Jiwei Li, Chun Fan

Pre-trained Natural Language Processing (NLP) models, which can be adapted to a variety of downstream language tasks via fine-tuning, highly accelerate the learning progress of NLP models. However, NLP models have been shown to be vulnerable to backdoor attacks. Previous NLP backdoor attacks mainly focus on one specific task. This limitation makes existing solutions less applicable to different NLP models which have been widely used in various tasks.
In this work, we propose BadPre, the first backdoor attack against various downstream models built based on pre-trained NLP models. BadPre can launch trojan attacks against different language tasks with the same trigger.
The key insight of our approach is that downstream models can inherit the security characteristics from the pre-trained models. Specifically, we leverage data posing to the pre-trained NLP models and then inference the downstream models with sentences embedded triggers. Furthermore, to fool backdoor detectors, we design a novel adversarial attack method to generate a more robust trigger.
Experimental results indicate that our approach can effectively attack a wide range of downstream NLP tasks and exhibit significant robustness against backdoor detectors.
An investigation of model-free planning
Authors: Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap

The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity. For an RL agent to address these challenges, it is essential that it can plan effectively. Prior work has typically utilized an explicit model of the environment, combined with a specific planning algorithm (such as tree search). More recently, a new family of methods have been proposed that learn how to plan, by providing the structure for planning via an inductive bias in the function approximator (such as a tree structured neural network), trained end-to-end by a model-free RL algorithm. In this paper, we go even further, and demonstrate empirically that an entirely model-free approach, without special structure beyond standard neural network components such as convolutional networks and LSTMs, can learn to exhibit many of the hallmarks that we would typically associate with a model-based planner.  We measure our agent's effectiveness at planning in terms of its ability to generalize across a combinatorial and irreversible state space, its data efficiency, and its ability to utilize additional thinking time. We find that our agent has the characteristics that one might expect to find in a planning algorithm. Furthermore, it exceeds the state-of-the-art in challenging combinatorial domains such as Sokoban and outperforms other model-free approaches that utilize strong inductive biases toward planning.
Learning advanced mathematical computations from examples
Authors: Francois Charton, Amaury Hayat, Guillaume Lample

Using transformers over large generated datasets, we train models to learn mathematical properties of differential systems, such as local stability, behavior at infinity and controllability. We achieve near perfect prediction of qualitative characteristics, and good approximations of numerical features of the system. This demonstrates that neural networks can learn to perform complex computations, grounded in advanced theory, from examples, without built-in mathematical knowledge.
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
Authors: Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud

Gradient-based optimization is the foundation of deep learning and reinforcement learning.
Even when the mechanism being optimized is unknown or not differentiable, optimization using high-variance or biased gradient estimates is still often the best strategy. We introduce a general framework for learning low-variance, unbiased gradient estimators for black-box functions of random variables, based on gradients of a learned function.
These estimators can be jointly trained with model parameters or policies, and are applicable in both discrete and continuous settings. We give unbiased, adaptive analogs of state-of-the-art reinforcement learning methods such as advantage actor-critic. We also demonstrate this framework for training discrete latent-variable models.
Learning Invariant Representations Of Planar Curves 
Authors: Gautam Pai, Aaron Wetzler, Ron Kimmel

We propose a metric learning framework for the construction of invariant geometric
functions of planar curves for the Euclidean and Similarity group of transformations.
We leverage on the representational power of convolutional neural
networks to compute these geometric quantities. In comparison with axiomatic
constructions, we show that the invariants approximated by the learning architectures
have better numerical qualities such as robustness to noise, resiliency to
sampling, as well as the ability to adapt to occlusion and partiality. Finally, we develop
a novel multi-scale representation in a similarity metric learning paradigm.
Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers
Authors: Narsimha Reddy Chilkuri, Eric Hunsberger, Aaron Russell Voelker, Gurshaant Singh Malik, Chris Eliasmith

Recent studies have demonstrated that the performance of transformers on the task of language modeling obeys a power-law relationship with model size over six orders of magnitude. While transformers exhibit impressive scaling, their performance hinges on processing large amounts of data, and their computational and memory requirements grow quadratically with sequence length. Motivated by these considerations, we construct a Legendre Memory Unit based model that introduces a general prior for sequence processing and exhibits an $O(n)$ and $O(n \ln n)$ (or better) dependency for memory and computation respectively. Over three orders of magnitude, we show that our new architecture attains the same accuracy as transformers with 10x fewer tokens.  We also show that for the same amount of training our model improves the loss over transformers about as much as transformers improve over LSTMs. Additionally, we demonstrate that adding global self-attention complements our architecture and the augmented model improves performance even further.
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
Authors: Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system, given by an ordinary differential equation (ODE), from observed state trajectories. To achieve better generalization with fewer training samples, SymODEN incorporates appropriate inductive bias by designing the associated computation graph in a physics-informed manner. In particular, we enforce Hamiltonian dynamics with control to learn the underlying dynamics in a transparent way, which can then be leveraged to draw insight about relevant physical aspects of the system, such as mass and potential energy. In addition, we propose a parametrization which can enforce this Hamiltonian formalism even when the generalized coordinate data is embedded in a high-dimensional space or we can only access velocity data instead of generalized momentum. This framework, by offering interpretable, physically-consistent models for physical systems, opens up new possibilities for synthesizing model-based control strategies.