nils-herrmann/clean_scirepeval huggingface dataset:
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness
Authors: Ke Sun, Mingjie Li, Zhouchen Lin

Adversarial robustness, mainly including sensitivity-based robustness and spatial robustness, plays an integral part in the robust generalization. In this paper, we endeavor to design strategies to achieve comprehensive adversarial robustness. To hit this target, firstly we investigate the less-studied spatial robustness and then integrate existing spatial robustness methods by incorporating both local and global spatial vulnerability into one spatial attack design. Based on this exploration, we further present a comprehensive relationship between natural accuracy, sensitivity-based and different spatial robustness, supported by the strong evidence from the perspective of representation. More importantly, in order to balance these mutual impact within different robustness into one unified framework, we incorporate the Pareto criterion into the adversarial robustness analysis, yielding a novel strategy towards comprehensive robustness called \textit{Pareto Adversarial Training}. The resulting Pareto front, the set of optimal solutions, provides the set of optimal balance among natural accuracy and different adversarial robustness, shedding light on solutions towards comprehensive robustness in the future. To the best of our knowledge, we are the first to consider comprehensive robustness via the multi-objective optimization.
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic and Sound
Authors: Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora

Saliency methods seek to provide human-interpretable explanations for the output of machine learning model on a given input. A plethora of saliency methods exist, as well as an extensive literature on their justifications/criticisms/evaluations. This paper focuses on heat maps based saliency methods that often provide explanations that look best to humans. It tries to introduce methods and evaluations for masked-based saliency methods that are {\em intrinsic} --- use just the training dataset and the trained net, and do not use separately trained nets, distractor distributions, human evaluations or annotations. Since a mask can be seen as a "certificate" justifying the net's answer, we introduce notions of {\em completeness} and {\em soundness} (the latter being the new contribution) motivated by logical proof systems. These notions allow a new evaluation of  saliency methods, that experimentally provides a novel and stronger justification for several heuristic tricks in the field (T.V. regularization, upscaling).
Lifelong Learning with Dynamically Expandable Networks
Authors: Jaehong Yoon, Eunho Yang, Jeongtae Lee, Sung Ju Hwang

We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them. We validate DEN on multiple public datasets in lifelong learning scenarios on multiple public datasets, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch model with substantially fewer number of parameters.
Parameter-Efficient Transfer Learning with Diff Pruning
Authors: Demi Guo, Alexander M Rush, Yoon Kim

While task-specific finetuning of deep networks pretrained with self-supervision has led to significant empirical advances in NLP, their large size makes the standard finetuning approach difficult to apply to multi-task, memory-constrained settings, as storing the full model parameters for each task become prohibitively expensive. We propose $\textit{diff pruning}$ as a simple approach to enable parameter-efficient transfer learning within the pretrain-finetune framework. This approach views finetuning as learning a task-specific diff vector that is applied on top of the pretrained parameter vector, which remains fixed and is shared across different tasks. The diff vector is adaptively pruned during training with a differentiable approximation to the $L_0$-norm penalty to encourage sparsity. Diff pruning becomes parameter-efficient as the number of tasks increases, as it requires storing only the nonzero positions and weights of the diff vector for each task, while the cost of storing the shared pretrained model remains constant. We find that models finetuned with diff pruning can match the performance of fully finetuned baselines on the GLUE benchmark while only modifying 0.5$\%$ of the pretrained model's parameters per task.
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
Authors: Paul Jeha, Michael Bohlke-Schneider, Pedro Mercado, Shubham Kapoor, Rajbir Singh Nirwan, Valentin Flunkert, Jan Gasthaus, Tim Januschowski

Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper we present PSA-GAN, a generative adversarial network (GAN) that generates long time series samples of high quality using progressive growing of GANs and self-attention. We show that PSA-GAN can be used to reduce the error in several downstream forecasting tasks over baselines that only use real data. We also introduce a Frechet-Inception Distance-like score for time series, Context-FID, assessing the quality of synthetic time series samples. We find that Context-FID is indicative for downstream performance. Therefore, Context-FID could be a useful tool to develop time series GAN models.
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding
Authors: Donghan Yu, Chenguang Zhu, Yiming Yang, Michael Zeng

Knowledge graphs (KGs) contain rich information about world knowledge, entities, and relations. Thus, they can be great supplements to existing pre-trained language models. However, it remains a challenge to efficiently integrate information from KG into language modeling. And the understanding of a knowledge graph requires related context. We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language. The knowledge module and language module provide essential information to mutually assist each other: the knowledge module produces embeddings for entities in text while the language module generates context-aware initial embeddings for entities and relations in the graph. Our design enables the pre-trained model to easily adapt to unseen knowledge graphs in new domains. Experimental results on several knowledge-aware NLP tasks show that our proposed framework achieves superior performance by effectively leveraging knowledge in language understanding.
Vision at A Glance: Interplay between Fine and Coarse Information Processing Pathways
Authors: Zilong Ji, Xiaolong Zou, Tiejun Huang, Si Wu

Object recognition is often viewed as a feedforward, bottom-up process in machine learning, but in real neural systems, object recognition is a complicated process which involves the interplay between two signal pathways. One is the parvocellular pathway (P-pathway), which is slow and extracts fine features of objects; the other is the magnocellular pathway (M-pathway), which is fast and extracts coarse features of objects. It has been suggested that the interplay between the two pathways endows the neural system with the capacity of processing visual information rapidly, adaptively, and robustly. However, the underlying computational mechanism remains largely unknown. In this study, we build a two-pathway model to elucidate the computational properties associated with the interactions between two visual pathways. The model consists of two convolution neural networks: one mimics the P-pathway, referred to as FineNet, which is deep, has small-size kernels, and receives detailed visual inputs; the other mimics the M-pathway, referred to as CoarseNet, which is shallow, has large-size kernels, and receives blurred visual inputs. 
The two pathways interact with each other to facilitate information processing. Specifically, we show that CoarseNet can learn from FineNet through imitation to improve its performance considerably, and that through feedback from CoarseNet, the performnace of FineNet is improved and becomes robust to noises. Using visual backward masking as an example, we demonstrate that our model can explain visual cognitive behaviors that involve the interplay between two pathways. We hope that this study will provide insight into understanding visual information processing and inspire the development of new object recognition architectures in machine learning.
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
Authors: Sheikh Shams Azam, Seyyedali Hosseinalipour, Qiang Qiu, Christopher Brinton

In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank characteristics of the subspace spanned by gradients (i.e., the gradient-space) in centralized model training, and observe that the gradient-space often consists of a few leading principal components accounting for an overwhelming majority (95-99%) of the explained variance. Motivated by this, we propose the "Look-back Gradient Multiplier" (LBGM) algorithm, which utilizes this low-rank property of the gradient-space in federated learning. Operationally, LBGM recycles the gradients between model update rounds to significantly reduce the number of parameters to be propagated through the system. We analytically characterize the convergence behavior of LBGM, revealing the nature of the trade-off between communication savings and model performance. Our subsequent experimental results demonstrate the improvement LBGM obtains on communication overhead compared to federated learning baselines. Additionally, we show that LBGM is a general plug-and-play algorithm that can be used standalone or stacked on top of existing sparsification techniques for distributed model training.
Machine Solver for Physics Word Problems
Authors: Megan Leszczynski, Jose Moreira

We build a machine solver for word problems on the physics of a free
falling object under constant acceleration of gravity.  Each problem
consists of a formulation part, describing the setting, and a question
part asking for the value of an unknown.  Our solver consists of
two long short-term memory recurrent neural networks and a numerical
integrator. The first neural network (the labeler) labels each
word of the problem, identifying the physical parameters and the
question part of the problem. The second neural network (the 
classifier) identifies what is being asked in the question. Using
the information extracted by both networks, the numerical integrator
computes the solution.  We observe that the classifier is resilient
to errors made by the labeler, which does a better job of identifying
the physics parameters than the question. Training, validation and test
sets of problems are generated from a grammar, with validation and test
problems structurally different from the training problems. The overall
accuracy of the solver on the test cases is 99.8%.
Neural Delay Differential Equations
Authors: Qunxi Zhu, Yao Guo, Wei Lin

Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets.  Recently, an augmented framework has been successfully developed for conquering some limitations emergent in application of the original framework.  Here we propose a new class of continuous-depth neural networks with delay, named as Neural Delay Differential Equations (NDDEs),  and, for computing the corresponding gradients, we use the adjoint sensitivity method to obtain the delayed dynamics of the adjoint. Since the differential equations with delays are usually seen as dynamical systems of infinite dimension possessing more fruitful dynamics, the NDDEs, compared to the NODEs, own a stronger capacity of nonlinear representations.  Indeed, we analytically validate that the NDDEs are of universal approximators, and further articulate an extension of the NDDEs, where the initial function of the NDDEs is supposed to satisfy ODEs.   More importantly, we use several illustrative examples to demonstrate the outstanding capacities of the NDDEs and the NDDEs with ODEs' initial value.  More precisely, (1) we successfully model the delayed dynamics where the trajectories in the lower-dimensional phase space could be mutually intersected, while the traditional NODEs without any argumentation are not directly applicable for such modeling, and (2) we achieve lower loss and higher accuracy not only for the  data produced synthetically by complex models but also for the real-world image datasets, i.e., CIFAR10, MNIST and SVHN.   Our results on the NDDEs reveal that appropriately articulating the elements of dynamical systems into the network design is truly beneficial to promoting the network performance.
Episodic Reinforcement Learning with Associative Memory
Authors: Guangxiang Zhu*, Zichuan Lin*, Guangwen Yang, Chongjie Zhang

Sample efficiency has been one of the major challenges for deep reinforcement learning. Non-parametric episodic control has been proposed to speed up parametric reinforcement learning by rapidly latching on previously successful policies. However, previous work on episodic reinforcement learning neglects the relationship between states and only stored the experiences as unrelated items. To improve sample efficiency of reinforcement learning, we propose a novel framework, called Episodic Reinforcement Learning with Associative Memory (ERLAM), which associates related experience trajectories to enable reasoning effective strategies. We build a graph on top of states in memory based on state transitions and develop a reverse-trajectory propagation strategy to allow rapid value propagation through the graph. We use the non-parametric associative memory as early guidance for a parametric reinforcement learning model. Results on navigation domain and Atari games show our framework achieves significantly higher sample efficiency than state-of-the-art episodic reinforcement learning models.
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
Authors: Shauharda Khadka, Estelle Aflalo, Mattias Mardar, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, Somdeb Majumdar

For deep neural network accelerators, memory movement is both energetically expensive and can bound computation. Therefore, optimal mapping of tensors to memory hierarchies is critical to performance. The growing complexity of neural networks calls for automated memory mapping instead of manual heuristic approaches; yet the search space of neural network computational graphs have previously been prohibitively large. We introduce Evolutionary Graph Reinforcement Learning (EGRL), a method designed for large search spaces, that combines graph neural networks, reinforcement learning, and evolutionary search. A set of fast, stateless policies guide the evolutionary search to improve its sample-efficiency. We train and validate our approach directly on the Intel NNP-I chip for inference. EGRL outperforms policy-gradient, evolutionary search and dynamic programming baselines on BERT, ResNet-101 and ResNet-50. We additionally achieve 28-78% speed-up compared to the native NNP-I compiler on all three workloads.
Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs
Authors: Naganand Yadati, Tingran Gao, Shahab Asoodeh, Partha Talukdar, Anand Louis

Graph-based semi-supervised learning (SSL) assigns labels to initially unlabelled vertices in a graph.
Graph neural networks (GNNs), esp. graph convolutional networks (GCNs), inspired the current-state-of-the art models for graph-based SSL problems.
GCNs inherently assume that the labels of interest are numerical or categorical variables.
However, in many real-world applications such as co-authorship networks, recommendation networks, etc., vertex labels can be naturally represented by probability distributions or histograms.
Moreover, real-world network datasets have complex relationships going beyond pairwise associations.
These relationships can be modelled naturally and flexibly by hypergraphs.
In this paper, we explore GNNs for graph-based SSL of histograms.
Motivated by complex relationships (those going beyond pairwise) in real-world networks, we propose a novel method for directed hypergraphs.
Our work builds upon existing works on graph-based SSL of histograms derived from the theory of optimal transportation.
A key contribution of this paper is to establish generalisation error bounds for a one-layer GNN within the framework of algorithmic stability.
We also demonstrate our proposed methods' effectiveness through detailed experimentation on real-world data.
We have made the code available.
Memformer: The Memory-Augmented Transformer
Authors: Qingyang Wu, Zhenzhong Lan, Jing Gu, Zhou Yu

Transformer models have obtained remarkable accomplishments in various NLP tasks. However, these models have efficiency issues on long sequences, as the complexity of their self-attention module scales quadratically with the sequence length. To remedy the limitation, we present Memformer, a novel language model that utilizes a single unified memory to encode and retrieve past information. It includes a new optimization scheme, Memory Replay Back-Propagation, which promotes long-range back-propagation through time with a significantly reduced memory requirement. Memformer achieves $\mathcal{O}(n)$ time complexity and $\mathcal{O}(1)$ space complexity in processing long sequences, meaning that the model can handle an infinite length sequence during inference. Our model is also compatible with other self-supervised tasks to further improve the performance on language modeling. Experimental results show that Memformer outperforms the previous long-range sequence models on WikiText-103, including Transformer-XL and Compressive Transformer.
Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos
Authors: Georgios Piliouras, Fang-Yi Yu

The recent framework of performative prediction is aimed at capturing settings where predictions influence the target/outcome they try to predict. In this paper, we introduce a natural multi-agent version of this framework, where multiple decision makers try to predict the same outcome.  We showcase that such competition can result in interesting phenomena by proving the possibility of phase transitions from stability to instability and eventually chaos. Specifically, we present settings of multi-agent performative prediction where under sufficient conditions, their dynamics lead to global stability and optimality. In the opposite direction, when the agents are not sufficiently cautious in their learning/updates rates, we show that instability and in fact formal chaos is possible. We complement our theoretical predictions with simulations showcasing the predictive power of our results.
A Rate-Distortion Theory of Adversarial Examples
Authors: Angus Galloway, Anna Golubeva, Graham W. Taylor

The generalization ability of deep neural networks (DNNs) is intertwined with model complexity, robustness, and capacity. Through establishing an equivalence between a DNN and a noisy communication channel, we characterize generalization and fault tolerance for unbounded adversarial attacks in terms of information-theoretic quantities. Invoking rate-distortion theory, we suggest that excess capacity is a significant cause of vulnerability to adversarial examples.
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR  AND INFERENCE ATTACKS
Authors: Thien Duc Nguyen, Phillip Rieger, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Ahmad-Reza Sadeghi, Thomas Schneider, Shaza Zeitouni

Recently, federated learning (FL) has been subject to both security and privacy attacks posing a dilemmatic challenge on the underlying algorithmic designs: On the one hand, FL is shown to be vulnerable to backdoor attacks that stealthily manipulate the global model output using malicious model updates, and on the other hand, FL is shown vulnerable to inference attacks by a malicious aggregator inferring information about clients’ data from their model updates. Unfortunately, existing defenses against these attacks are insufficient and mitigating both attacks at the same time is highly challenging, because while defeating backdoor attacks requires the analysis of model updates, protection against inference attacks prohibits access to the model updates to avoid information leakage. In this work, we introduce BAFFLE, a novel in-depth defense for FL that tackles this challenge. To mitigate backdoor attacks, it applies a multilayered defense by using a Model Filtering layer to detect and reject malicious model updates and a Poison Elimination layer to eliminate any effect of a remaining undetected weak manipulation. To impede inference attacks, we build private BAFFLE that securely evaluates the BAFFLE algorithm under encryption using sophisticated secure computation techniques. We extensively evaluate BAFFLE against state-of-the-art backdoor attacks on several datasets and applications, including image classification, word prediction, and IoT intrusion. We show that BAFFLE can entirely remove backdoors with a negligible effect on accuracy and that private BAFFLE is practical.
Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks
Authors: Giuseppe Marra, Dario Zanca, Alessandro Betti, Marco Gori

The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles. There is also clear evidence that the activation function (e.g. the rectiﬁer and the LSTM units) plays a crucial role in the complexity of learning. Based on this remark, this paper discusses an optimal selection of the neuron non-linearity in a functional framework that is inspired from classic regularization arguments. A representation theorem is given which indicates that the best activation function is a kernel expansion in the training set, that can be effectively approximated over an opportune set of points modeling 1-D clusters. The idea can be naturally extended to recurrent networks, where the expressiveness of kernel-based activation functions turns out to be a crucial ingredient to capture long-term dependencies. We give experimental evidence of this property by a set of challenging experiments, where we compare the results with neural architectures based on state of the art LSTM cells.