nils-herrmann/clean_scirepeval huggingface dataset:
Fourier Neural Operator for Parametric Partial Differential Equations
Authors: Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces.  Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus, they learn an entire family of PDEs, in contrast to classical methods which solve one instance of the equation. In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture. We perform experiments on Burgers' equation, Darcy flow, and Navier-Stokes equation. The Fourier neural operator is the first ML-based method to successfully model turbulent flows with zero-shot super-resolution. It is up to three orders of magnitude faster compared to traditional PDE solvers. Additionally, it achieves superior accuracy compared to previous learning-based solvers under fixed resolution.
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
Authors: Xinyue Chen, Che Wang, Zijian Zhou, Keith W. Ross

Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks. In this paper, we introduce a simple model-free algorithm, Randomized Ensembled Double Q-Learning (REDQ), and show that its performance is just as good as, if not better than, a state-of-the-art model-based algorithm for the MuJoCo benchmark. Moreover, REDQ can achieve this performance using fewer parameters than the model-based method, and with less wall-clock run time. REDQ has three carefully integrated ingredients which allow it to achieve its high performance: (i) a UTD ratio $\gg 1$; (ii) an ensemble of Q functions; (iii) in-target minimization across a random subset of Q functions from the ensemble. Through carefully designed experiments, we provide a detailed analysis of REDQ and related model-free algorithms. To our knowledge, REDQ is the first successful model-free DRL algorithm for continuous-action spaces using a UTD ratio $\gg
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior
Authors: Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu

Denoising diffusion probabilistic models have been recently proposed to generate high-quality samples by estimating the gradient of the data density. The framework assumes the prior noise as a standard Gaussian distribution, whereas the corresponding data distribution may be more complicated than the standard Gaussian distribution, which potentially introduces inefficiency in denoising the prior noise into the data sample because of the discrepancy between the data and the prior. In this paper, we propose PriorGrad to improve the efficiency of the conditional diffusion model (for example, a vocoder using a mel-spectrogram as the condition) by applying an adaptive prior derived from the data statistics based on the conditional information. We formulate the training and sampling procedures of PriorGrad and demonstrate the advantages of an adaptive prior through a theoretical analysis. Focusing on the audio domain, we consider the recently proposed diffusion-based audio generative models based on both the spectral and time domains and show that PriorGrad achieves faster convergence and superior performance, leading to an improved perceptual quality and tolerance to a smaller network capacity, and thereby demonstrating the efficiency of a data-dependent adaptive prior.
Privacy Implications of Shuffling
Authors: Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha

\ldp deployments are vulnerable to inference attacks as an adversary can link the noisy responses to their identity and subsequently, auxiliary information using the \textit{order} of the data. An alternative model, shuffle \textsf{DP}, prevents this by shuffling the noisy responses uniformly at random.  However, this limits the data learnability -- only symmetric functions (input order agnostic) can be learned. In this paper, we strike a balance and show that systematic shuffling of the noisy responses can thwart specific inference attacks while retaining some meaningful data learnability. To this end, we propose a novel privacy guarantee, \name-privacy, that captures the privacy of the order of a data sequence. \name-privacy allows tuning the granularity at which the ordinal information is maintained, which formalizes the degree the resistance to inference attacks trading it off with data learnability.  Additionally, we propose a novel shuffling mechanism that can achieve \name-privacy and demonstrate the practicality of our mechanism via evaluation on real-world datasets.
Return-Based Contrastive Representation Learning for Reinforcement  Learning
Authors: Guoqing Liu, Chuheng Zhang, Li Zhao, Tao Qin, Jinhua Zhu, Li Jian, Nenghai Yu, Tie-Yan Liu

Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxiliary tasks do not take the characteristics of RL problems into consideration and are unsupervised. By leveraging returns, the most important feedback signals in RL, we propose a novel auxiliary task that forces the learnt representations to discriminate state-action pairs with different returns. Our auxiliary loss is theoretically justified to learn representations that capture the structure of a new form of state-action abstraction, under which state-action pairs with similar return distributions are aggregated together. Empirically, our algorithm outperforms strong baselines on complex tasks in Atari games and DeepMind Control suite, and achieves even better performance when combined with existing auxiliary tasks.
Dynamic Early Terminating of Multiply Accumulate Operations for Saving Computation Cost in Convolutional Neural Networks
Authors: Yu-Yi Su, Yung-Chih Chen, Xiang-Xiu Wu, Shih-Chieh Chang

Deep learning has been attracting enormous attention from academia as well as industry due to its great success in many artificial intelligence applications. As more applications are developed, the need for implementing a complex neural network model on an energy-limited edge device becomes more critical. To this end, this paper proposes a new optimization method to reduce the computation efforts of convolutional neural networks. The method takes advantage of the fact that some convolutional operations are actually wasteful since their outputs are pruned by the following activation or pooling layers. Basically, a convolutional filter conducts a series of multiply-accumulate (MAC) operations. We propose to set a checkpoint in the MAC process to determine whether a filter could terminate early based on the intermediate result. Furthermore, a fine-tuning process is conducted to recover the accuracy drop due to the applied checkpoints. The experimental results show that the proposed method can save approximately 50% MAC operations with less than 1% accuracy drop for CIFAR-10 example model and Network in Network on the CIFAR-10 and CIFAR-100 datasets. Additionally, compared with the state-of- the-art method, the proposed method is more effective on the CIFAR-10 dataset and is competitive on the CIFAR-100 dataset.
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
Authors: David Henry Mguni, Joel Jennings, Taher Jafferjee, Aivar Sootla, Changmin Yu, Usman Islam, Ziyan Wang, Yaodong Yang, Jun Wang

Exploring in an unknown system can place an agent in dangerous situations,
exposing to potentially catastrophic hazards. Many current approaches for tackling
safe learning in reinforcement learning (RL) lead to a trade-off between safe
exploration and fulfilling the task. Though these methods possibly incur fewer
safety violations they often also lead to reduced task performance. In this paper, we
take the first step in introducing a generation of RL solvers that learn to minimise
safety violations while maximising the task reward to the extend that can be
tolerated by safe policies. Our approach uses a new two-player framework for safe
RL called DESTA. The core of DESTA is a novel game between two RL agents:
Safety Agent that is delegated the task of minimising safety violations and Task
Agent whose goal is to maximise the reward set by the environment task. Safety
Agent can selectively take control of the system at any given point to prevent
safety violations while Task Agent is free to execute its actions at all other states.
This framework enables Safety Agent to learn to take actions that minimise future
safety violations (during and after training) by performing safe actions at certain
states while Task Agent performs actions that maximise the task performance
everywhere else. We demonstrate DESTA’s ability to tackle challenging tasks and
compare against state-of-the-art RL methods in Safety Gym Benchmarks which
simulate real-world physical systems and OpenAI’s Lunar Lander.
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning
Authors: Tae Hyun Cho, Sungyeob Han, Heesoo Lee, Kyungjae Lee, Jungwoo Lee

Distributional reinforcement learning aims to learn distribution of return under stochastic environments. Since the learned distribution of return contains rich information about the stochasticity of the environment, previous studies have relied on descriptive statistics, such as standard deviation, for optimism in face of uncertainty. These prior works are divided into risk-seeking or averse methods, which can be considered as having a one-sided tendency on risk. Unexpectedly, such approaches hinder convergence. In this paper, we propose a novel distributional reinforcement learning that explores by randomizing the risk criterion to reach a risk-neutral optimal policy. First, we provide a perturbed distributional Bellman optimality operator by distorting the risk measure in action selection. Second, we prove the convergence and optimality of the proposed method by using weaker contraction property. Our theoretical results support that the proposed method does not fall into biased exploration and converges to an optimal return distribution. Finally, we empirically show that our method outperforms other existing distribution-based algorithms in various environments including Atari games.
Residual Connections Encourage Iterative Inference
Authors: Stanisław Jastrzebski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, Yoshua Bengio

Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. A recent view argues that Resnets perform iterative refinement of features. We attempt to further expose properties of this aspect. To this end, we study Resnets both analytically and empirically. We formalize the notion of iterative refinement in Resnets by showing that residual architectures naturally encourage features to move along the negative gradient of loss during the feedforward phase. In addition, our empirical analysis suggests that Resnets are able to perform both representation learning and iterative refinement. In general, a Resnet block tends to concentrate representation learning behavior in the first few layers while higher layers perform iterative refinement of features. Finally we observe that sharing residual layers naively leads to representation explosion and hurts generalization performance, and show that simple existing strategies can help alleviating this problem.
A closer look at the word analogy problem
Authors: Siddharth Krishna Kumar

Although word analogy problems have become a standard tool for evaluating word vectors, little is known about why word vectors are so good at solving these problems. In this paper, I attempt to further our understanding of the subject, by developing a simple, but highly accurate generative approach to solve the word analogy problem for the case when all terms involved in the problem are nouns. My results demonstrate the ambiguities associated with learning the relationship between a word pair, and the role of the training dataset in determining the relationship which gets most highlighted. Furthermore, my results show that the ability of a model to accurately solve the word analogy problem may not be indicative of a model’s ability to learn the relationship between a word pair the way a human does.
Top-N: Equivariant Set and Graph Generation without Exchangeability
Authors: Clement Vignac, Pascal Frossard

This work addresses one-shot set and graph generation, and, more specifically, the parametrization of probabilistic decoders that map a vector-shaped prior to a distribution over sets or graphs. Sets and graphs are most commonly generated by first sampling points i.i.d. from a normal distribution, and then processing these points along with the prior vector using Transformer layers or Graph Neural Networks. 
This architecture is designed to generate exchangeable distributions, i.e., all permutations of the generated outputs are equally likely. We however show that it only optimizes a proxy to the evidence lower bound, which makes it hard to train. We then study equivariance in generative settings and show that non-exchangeable methods can still achieve permutation equivariance. Using this result, we introduce Top-n creation, a differentiable generation mechanism that uses the latent vector to select the most relevant points from a trainable reference set. Top-n can replace i.i.d. generation in any Variational Autoencoder or Generative Adversarial Network. Experimentally, our method outperforms i.i.d. generation by 15% at SetMNIST reconstruction, by 33% at object detection on CLEVR, generates sets that are 74% closer to the true distribution on a synthetic molecule-like dataset, and generates more valid molecules on QM9.
Increasing the Cost of Model Extraction with Calibrated Proof of Work
Authors: Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot

In model extraction attacks, adversaries can steal a machine learning model exposed via a public API by repeatedly querying it and adjusting their own model based on obtained predictions. To prevent model stealing, existing defenses focus on detecting malicious queries, truncating, or distorting outputs, thus necessarily introducing a tradeoff between robustness and model utility for legitimate users. Instead, we propose to impede model extraction by requiring users to complete a proof-of-work before they can read the model's predictions. This deters attackers by greatly increasing (even up to 100x) the computational effort needed to leverage query access for model extraction. Since we calibrate the effort required to complete the proof-of-work to each query, this only introduces a slight overhead for regular users (up to 2x). To achieve this, our calibration applies tools from differential privacy to measure the information revealed by a query. Our method requires no modification of the victim model and can be applied by machine learning practitioners to guard their publicly exposed models against being easily stolen.
Learning One-hidden-layer Neural Networks with Landscape Design
Authors: Rong Ge, Jason D. Lee, Tengyu Ma

We consider the problem of learning a one-hidden-layer neural network: we assume the input x is from Gaussian distribution and the label $y = a \sigma(Bx) + \xi$, where a is a nonnegative vector and  $B$ is a full-rank weight matrix, and $\xi$ is a noise vector. We first give an analytic formula for the population risk of the standard squared loss and demonstrate that it implicitly attempts to decompose a sequence of low-rank tensors simultaneously. 
	
Inspired by the formula, we design a non-convex objective function $G$ whose landscape is guaranteed to have the following properties:	

1. All local minima of $G$ are also global minima.
2. All global minima of $G$ correspond to the ground truth parameters.
3. The value and gradient of $G$ can be estimated using samples.
	
With these properties, stochastic gradient descent on $G$ provably converges to the global minimum and learn the ground-truth parameters. We also prove finite sample complexity results and validate the results by simulations.
Defective Convolutional Networks
Authors: Tiange Luo, Tianle Cai, Mengxiao Zhang, Siyu Chen, Di He, Liwei Wang

Robustness of convolutional neural networks (CNNs) has gained in importance on account of adversarial examples, i.e., inputs added as well-designed perturbations that are imperceptible to humans but can cause the model to predict incorrectly. Recent research suggests that the noise in adversarial examples breaks the textural structure, which eventually leads to wrong predictions. To mitigate the threat of such adversarial attacks, we propose defective convolutional networks that make predictions relying less on textural information but more on shape information by properly integrating defective convolutional layers into standard CNNs. The defective convolutional layers contain defective neurons whose activations are set to be a constant function. As defective neurons contain no information and are far different from standard neurons in its spatial neighborhood, the textural features cannot be accurately extracted, and so the model has to seek other features for classification, such as the shape. We show extensive evidence to justify our proposal and demonstrate that defective CNNs can defend against black-box attacks better than standard CNNs. In particular, they achieve state-of-the-art performance against transfer-based attacks without any adversarial training being applied.
Dimensional Reweighting Graph Convolution Networks
Authors: Xu Zou, Qiuye Jia, Jianwei Zhang, Chang Zhou, Zijun Yao, Hongxia Yang, Jie Tang

In this paper, we propose a method named Dimensional reweighting Graph Convolutional Networks (DrGCNs), to tackle the problem of variance between dimensional information in the node representations of GCNs. We prove that DrGCNs can reduce the variance of the node representations by connecting our problem to the theory of the mean field. However, practically, we find that the degrees DrGCNs help vary severely on different datasets. We revisit the problem and develop a new measure K to quantify the effect. This measure guides when we should use dimensional reweighting in GCNs and how much it can help. Moreover, it offers insights to explain the improvement obtained by the proposed DrGCNs. The dimensional reweighting block is light-weighted and highly flexible to be built on most of the GCN variants. Carefully designed experiments, including several fixes on duplicates, information leaks, and wrong labels of the well-known node classification benchmark datasets, demonstrate the superior performances of DrGCNs over the existing state-of-the-art approaches. Significant improvements can also be observed on a large scale industrial dataset.
NeuroFabric: Identifying Ideal Topologies for Training A Priori Sparse Networks
Authors: Mihailo Isakov, Michel A. Kinsy

Long training times of deep neural networks are a bottleneck in machine learning research. The major impediment to fast training is the quadratic growth of both memory and compute requirements of dense and convolutional layers with respect to their information bandwidth. Recently, training `a priori' sparse networks has been proposed as a method for allowing layers to retain high information bandwidth, while keeping memory and compute low. However, the choice of which sparse topology should be used in these networks is unclear. In this work, we provide a theoretical foundation for the choice of intra-layer topology. First, we derive a new sparse neural network initialization scheme that allows us to explore the space of very deep sparse networks. Next, we evaluate several topologies and show that seemingly similar topologies can often have a large difference in attainable accuracy. To explain these differences, we develop a data-free heuristic that can evaluate a topology independently from the dataset the network will be trained on. We then derive a set of requirements that make a good topology, and arrive at a single topology that satisfies all of them.
Convolutional Neural Networks Generalization Utilizing the Data Graph Structure
Authors: Yotam Hechtlinger, Purvasha Chakravarti, Jining Qin

Convolutional Neural Networks have proved to be very efficient in image and audio processing. Their success is mostly attributed to the convolutions which utilize the geometric properties of a low - dimensional grid structure. This paper suggests a generalization of CNNs to graph-structured data with varying graph structure, that can be applied to standard regression or classification problems by learning the graph structure of the data. We propose a novel convolution framework approach on graphs which utilizes a random walk to select relevant nodes. The convolution shares weights on all features, providing the desired parameter efficiency.  Furthermore, the additional computations in the training process are only executed once in the pre-processing step.  We empirically demonstrate the performance of the proposed CNN on MNIST data set, and challenge the state-of-the-art on Merck molecular activity data set.
A Greedy Approach to Max-Sliced Wasserstein GANs
Authors: András Horváth

Generative Adversarial Networks have made data generation possible in various use cases, but in case of complex, high-dimensional distributions it can be difficult to train them, because of convergence problems and the appearance of mode collapse.
Sliced Wasserstein GANs and especially the application of the Max-Sliced Wasserstein distance made it possible to approximate Wasserstein distance during training in an efficient and stable way and helped ease convergence problems of these architectures.

This method transforms sample assignment and distance calculation into sorting the one-dimensional projection of the samples, which results a sufficient approximation of the high-dimensional Wasserstein distance. 

In this paper we will demonstrate that the approximation of the Wasserstein distance by sorting the samples is not always the optimal approach and the greedy assignment of the real and fake samples can result faster convergence and better approximation of the original distribution.
Neural Operator Search
Authors: Wei Li, Shaogang Gong, Xiatian Zhu

Existing neural architecture search (NAS) methods explore a limited feature-transformation-only search space while ignoring other advanced feature operations such as feature self-calibration by attention and dynamic convolutions. This disables the NAS algorithms to discover more advanced network architectures. We address this limitation by additionally exploiting feature self-calibration operations, resulting in a heterogeneous search space. To solve the challenges of operation heterogeneity and significantly larger search space, we formulate a neural operator search (NOS) method. NOS presents a novel heterogeneous residual block for integrating the heterogeneous operations in a unified structure, and an attention guided search strategy for facilitating the search process over a vast space. Extensive experiments show that NOS can search novel cell architectures with highly competitive performance on the CIFAR and ImageNet benchmarks.