nils-herrmann/clean_scirepeval huggingface dataset:
ON BREIMAN’S DILEMMA IN NEURAL NETWORKS: SUCCESS AND FAILURE OF NORMALIZED MARGINS
Authors: Yifei HUANG, Yuan YAO, Weizhi ZHU

A belief persists long in machine learning that enlargement of margins over training data accounts for the resistance of models to overfitting by increasing the robustness. Yet Breiman shows a dilemma (Breiman, 1999) that a uniform improvement on margin distribution \emph{does not} necessarily reduces generalization error. In this paper, we revisit Breiman's dilemma in deep neural networks with recently proposed normalized margins using Lipschitz constant bound by spectral norm products. With both simplified theory and extensive experiments, Breiman's dilemma is shown to rely on dynamics of normalized margin distributions, that reflects the trade-off between model expression power and data complexity. When the complexity of data is comparable to the model expression power in the sense that training and test data share similar phase transitions in normalized margin dynamics, two efficient ways are derived via classic margin-based generalization bounds to successfully predict the trend of generalization error. On the other hand, over-expressed models that exhibit uniform improvements on training normalized margins may lose such a prediction power and fail to prevent the overfitting.
At Your Fingertips: Automatic Piano Fingering Detection
Authors: Amit Moryossef, Yanai Elazar, Yoav Goldberg

Automatic Piano Fingering is a hard task which computers can learn using data. As data collection is hard and expensive, we propose to automate this process by automatically extracting fingerings from public videos and MIDI files, using computer-vision techniques. Running this process on 90 videos results in the largest dataset for piano fingering with more than 150K notes. We show that when running a previously proposed model for automatic piano fingering on our dataset and then fine-tuning it on manually labeled piano fingering data, we achieve state-of-the-art results.
In addition to the fingering extraction method, we also introduce a novel method for transferring deep-learning computer-vision models to work on out-of-domain data, by fine-tuning it on out-of-domain augmentation proposed by a Generative Adversarial Network (GAN).

For demonstration, we anonymously release a visualization of the output of our process for a single video on
Graph Pooling by Edge Cut
Authors: Alexis Galland, marc lelarge

Graph neural networks (GNNs) are very efficient at solving several tasks in graphs such as node classification or graph classification. They come from an adaptation of convolutional neural networks on images to graph structured data. These models are very effective at finding patterns in images that can discriminate images from each others. Another aspect leading to their success is their ability to uncover hierarchical structures. This comes from the pooling operation that produces different versions of the input image at different scales. The same way, we want to identify patterns at different scales in graphs in order to improve the classification accuracy. Compared to the case of images, it is not trivial to develop a pooling layer on graphs. This is mainly due to the fact that in graphs nodes are not ordered and have irregular neighborhoods. To aleviate this issue, we propose a pooling layer based on edge cuts in graphs. This pooling layer works by computing edge scores that correspond to the importance of edges in the process of information propagation of the GNN. Moreover, we define a regularization function that aims at producing edge scores that minimize the minCUT problem. Finally, through extensive experiments we show that this architecture can compete with state-of- the-art methods.
Learning to Protect Communications with Adversarial Neural Cryptography
Authors: Martín Abadi, David G. Andersen

We ask whether neural networks can learn to use secret keys to protect
information from other neural networks.  Specifically, we focus on
ensuring confidentiality properties in a multiagent system, and we
specify those properties in terms of an adversary.  Thus, a
system may consist of neural networks named Alice and Bob, and we aim
to limit what a third neural network named Eve learns from
eavesdropping on the communication between Alice and Bob.
We do not prescribe specific cryptographic algorithms to these neural networks;
instead, we train end-to-end, adversarially.
We demonstrate that the neural networks can learn 
how to perform forms of encryption and decryption, and also
how to apply these operations selectively in order to meet
confidentiality goals.
BOSH: An Efficient Meta Algorithm for Decision-based Attacks
Authors: Zhenxin Xiao, Puyudi Yang, Yuchen Jiang, Kai-Wei Chang, Cho-Jui Hsieh

Adversarial example generation becomes a viable method for evaluating the robustness of a machine learning model. In this paper, we consider hard-label black- box attacks (a.k.a. decision-based attacks), which is a challenging setting that generates adversarial examples based on only a series of black-box hard-label queries. This type of attacks can be used to attack discrete and complex models, such as Gradient Boosting Decision Tree (GBDT) and detection-based defense models. Existing decision-based attacks based on iterative local updates often get stuck in a local minimum and fail to generate the optimal adversarial example with the smallest distortion. To remedy this issue, we propose an efficient meta algorithm called BOSH-attack, which tremendously improves existing algorithms through Bayesian Optimization (BO) and Successive Halving (SH). In particular, instead of traversing a single solution path when searching an adversarial example, we maintain a pool of solution paths to explore important regions. We show empirically that the proposed algorithm converges to a better solution than existing approaches, while the query count is smaller than applying multiple random initializations by a factor of 10.
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering
Authors: Calypso Herrera, Florian Krach, Josef Teichmann

Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outperform existing discrete-time approaches, no theoretical guarantees for their predictive capabilities are available. Assuming that the irregularly-sampled time series data originates from a continuous stochastic process, the $L^2$-optimal online prediction is the conditional expectation given the currently available information. We introduce the Neural Jump ODE (NJ-ODE) that provides a data-driven approach to learn, continuously in time, the conditional expectation of a stochastic process. Our approach models the conditional expectation between two observations with a neural ODE and jumps whenever a new observation is made. We define a novel training framework, which allows us to prove theoretical guarantees for the first time. In particular, we show that the output of our model converges to the $L^2$-optimal prediction. This can be interpreted as solution to a special filtering problem. We provide experiments showing that the theoretical results also hold empirically. Moreover, we experimentally show that our model outperforms the baselines in more complex learning tasks and give comparisons on real-world datasets.
Understanding Why Neural Networks Generalize Well Through GSNR of Parameters
Authors: Jinlong Liu, Yunzhi Bai, Guoqing Jiang, Ting Chen, Huayan Wang

As deep neural networks (DNNs) achieve tremendous success across many application domains, researchers tried to explore in many aspects on why they generalize well. In this paper, we provide a novel perspective on these issues using the gradient signal to noise ratio (GSNR) of parameters during training process of DNNs. The GSNR of a parameter is simply defined as the ratio between its gradient's squared mean and variance, over the data distribution. Based on several approximations, we establish a quantitative relationship between model parameters' GSNR and the generalization gap. This relationship indicates that larger GSNR during training process leads to better generalization performance. Futher, we show that, different from that of shallow models (e.g. logistic regression, support vector machines), the gradient descent optimization dynamics of DNNs naturally produces large GSNR during training, which is probably the key to DNNs’ remarkable generalization ability.
Interpolation-Prediction Networks for Irregularly Sampled Time Series
Authors: Satya Narayan Shukla, Benjamin Marlin

In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models.
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Authors: Mrigank Raman, Aaron Chan, Siddhant Agarwal, PeiFeng Wang, Hansen Wang, Sungchul Kim, Ryan Rossi, Handong Zhao, Nedim Lipka, Xiang Ren

Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation. By using attention over the KG, such KG-augmented models can also "explain" which KG information was most relevant for making a given prediction. In this paper, we question whether these models are really behaving as we expect. We show that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs, which maintain the downstream performance of the original KG while significantly deviating from the original KG's semantics and structure. Our findings raise doubts about KG-augmented models' ability to reason about KG information and give sensible explanations.
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
Authors: Leslie O'Bray, Max Horn, Bastian Rieck, Karsten Borgwardt

Graph generative models are a highly active branch of machine learning. Given the steady development of new models of ever-increasing complexity, it is necessary to provide a principled way to evaluate and compare them. In this paper, we enumerate the desirable criteria for such a comparison metric and provide an overview of the status quo of graph generative model comparison in use today, which predominantly relies on the maximum mean discrepancy (MMD). We perform a systematic evaluation of MMD in the context of graph generative model comparison, highlighting some of the challenges and pitfalls researchers inadvertently may encounter. After conducting a thorough analysis of the behaviour of MMD on synthetically-generated perturbed graphs as well as on recently-proposed graph generative models, we are able to provide a suitable procedure to mitigate these challenges and pitfalls. We aggregate our findings into a list of practical recommendations for researchers to use when evaluating graph generative models.
Byzantine-Resilient Non-Convex Stochastic Gradient Descent
Authors: Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li, Dan Alistarh

We study adversary-resilient stochastic distributed optimization, in which $m$ machines can independently compute stochastic gradients, and cooperate to jointly optimize over their local objective functions. However, an $\alpha$-fraction of the machines are Byzantine, in that they may behave in arbitrary, adversarial ways. We consider a variant of this procedure in the challenging non-convex case. Our main result is a new algorithm SafeguardSGD, which can provably escape saddle points and find approximate local minima of the non-convex objective. The algorithm is based on a new concentration filtering technique, and its sample and time complexity bounds match the best known theoretical bounds in the stochastic, distributed setting when no Byzantine machines are present. 

Our algorithm is very practical: it improves upon the performance of all prior methods when training deep neural networks, it is relatively lightweight, and it is the first method to withstand two recently-proposed Byzantine attacks.
Attack-Resistant Federated Learning with Residual-based Reweighting
Authors: Shuhao Fu, Chulin Xie, Bo Li, Qifeng Chen

Federated learning has a variety of applications in multiple domains by utilizing private training data stored on different devices. However, the aggregation process in federated learning is highly vulnerable to adversarial attacks so that the global model may behave abnormally under attacks. To tackle this challenge, we present a novel aggregation algorithm with residual-based reweighting to defend federated learning. Our aggregation algorithm combines repeated median regression with the reweighting scheme in iteratively reweighted least squares. Our experiments show that our aggression algorithm outperforms other alternative algorithms in the presence of label-flipping, backdoor, and Gaussian noise attacks. We also provide theoretical guarantees for our aggregation algorithm.
Spatially Structured Recurrent Modules
Authors: Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution. While methods that harness spatial and temporal structures find broad application, recent work has demonstrated the potential of models that leverage sparse and modular structure using an ensemble of sparingly interacting modules. In this work, we take a step towards dynamic models that are capable of simultaneously exploiting both modular and spatiotemporal structures. To this end, we model the dynamical system as a collection of autonomous but sparsely interacting sub-systems that interact according to a learned topology which is informed by the spatial structure of the underlying system. This gives rise to a class of models that are well suited for capturing the dynamics of systems that only offer local views into their state, along with corresponding spatial locations of those views. On the tasks of video prediction from cropped frames and multi-agent world modelling from partial observations in the challenging Starcraft2 domain, we find our models to be more robust to the number of available views and better capable of generalisation to novel tasks without additional training than strong baselines that perform equally well or better on the training distribution.
Fast Model Editing at Scale
Authors: Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D Manning

While large pre-trained models have enabled impressive results on a variety of downstream tasks, the largest existing models still make errors, and even accurate predictions may become outdated over time. Because detecting all such failures at training time is impossible, enabling both developers and end users of such models to correct inaccurate outputs while leaving the model otherwise intact is desirable. However, the distributed, black-box nature of the representations learned by large neural networks makes producing such targeted edits difficult. If presented with only a single problematic input and new desired output, fine-tuning approaches tend to overfit; other editing algorithms are either computationally infeasible or simply ineffective when applied to very large models. To enable easy post-hoc editing at scale, we propose Model Editor Networks using Gradient Decomposition (MEND), a collection of small auxiliary editing networks that use a single desired input-output pair to make fast, local edits to a pre-trained model's behavior. MEND learns to transform the gradient obtained by standard fine-tuning, using a low-rank decomposition of the gradient to make the parameterization of this transformation tractable. MEND can be trained on a single GPU in less than a day even for 10 billion+ parameter models; once trained MEND enables rapid application of new edits to the pre-trained model. Our experiments with T5, GPT, BERT, and BART models show that MEND is the only approach to model editing that effectively edits the behavior of models with more than 10 billion parameters. Code available at https://sites.google.com/view/mend-editing.
Imperceptible Black-box Attack via Refining in Salient Region
Authors: Zeyu Dai, Shengcai Liu, Ke Tang, Qing Li

Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker only has query access to the model output. Recent studies have devised successful black-box attacks with high query efficiency. However, such performance often comes at the cost of the imperceptibility of adversarial attacks, which is essential for attackers. To address this issue, in this paper we propose to use segmentation priors for black-box attacks such that the perturbations are limited in the salient region. We find that state-of-the-art black-box attacks equipped with segmentation priors can achieve much better imperceptibility performance with little reduction in query efficiency and success rate. We further propose the Saliency Attack, a new gradient-free black-box attack that can further improve the imperceptibility by refining perturbations in the salient region. Experimental results show that the perturbations generated by our approach are much more imperceptible than the ones generated by other attacks, and are interpretable to some extent. Furthermore, our approach is found to be more robust to detection-based defense, which demonstrates its efficacy as well.
Simple and efficient architecture search for Convolutional Neural Networks
Authors: Thomas Elsken, Jan Hendrik Metzen, Frank Hutter

Neural networks have recently had a lot of success for many tasks. However, neural
network architectures that perform well are still typically designed manually
by experts in a cumbersome trial-and-error process. We propose a new method
to automatically search for well-performing CNN architectures based on a simple
hill climbing procedure whose operators apply network morphisms, followed
by short optimization runs by cosine annealing. Surprisingly, this simple method
yields competitive results, despite only requiring resources in the same order of
magnitude as training a single network. E.g., on CIFAR-10, our method designs
and trains networks with an error rate below 6% in only 12 hours on a single GPU;
training for one day reduces this error further, to almost 5%.
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
Authors: Lanqing Li, Rui Yang, Dijun Luo

We study the offline meta-reinforcement learning (OMRL) problem, a paradigm which enables reinforcement learning (RL) algorithms to quickly adapt to unseen tasks without any interactions with the environments, making RL truly practical in many real-world applications. This problem is still not fully understood, for which two major challenges need to be addressed. First, offline RL usually suffers from bootstrapping errors of out-of-distribution state-actions which leads to divergence of value functions. Second, meta-RL requires efficient and robust task inference learned jointly with control policy. In this work, we enforce behavior regularization on learned policy as a general approach to offline RL, combined with a deterministic context encoder for efficient task inference. We propose a novel negative-power distance metric on bounded context embedding space, whose gradients propagation is detached from the Bellman backup. We provide analysis and insight showing that some simple design choices can yield substantial improvements over recent approaches involving meta-RL and distance metric learning. To the best of our knowledge, our method is the first model-free and end-to-end OMRL algorithm, which is computationally efficient and demonstrated to outperform prior algorithms on several meta-RL benchmarks.
Independent Component Alignment for Multi-task Learning
Authors: Dmitry Senushkin, Iaroslav Melekhov, Mikhail Romanov, Anton Konushin, Juho Kannala, Arno Solin

We present a novel gradient-based multi-task learning (MTL) approach that balances training in multi-task systems by aligning the independent components of the training objective. In contrast to state-of-the-art MTL approaches, our method is stable and preserves the ratio of highly correlated tasks gradients. The method is scalable, reduces overfitting, and can seamlessly handle multi-task objectives with a large difference in gradient magnitudes. We demonstrate the effectiveness of the proposed approach on a variety of MTL problems including digit classification, multi-label image classification, camera relocalization, and scene understanding. Our approach performs favourably compared to other gradient-based adaptive balancing methods, and its performance is backed up by theoretical analysis.
Consistent Instance Classification for Unsupervised Representation Learning
Authors: Depu Meng, Zigang Geng, Zhirong Wu, Bin Xiao, Houqiang Li, Jingdong Wang

In this paper, we address the problem of learning the representations from images without human annotations. We study the instance classification solution, which regards each instance as a category, and improve the optimization and feature quality. The proposed consistent instance classification (ConIC) approach simultaneously optimizes the classification loss and an additional consistency loss explicitly penalizing the feature dissimilarity between the augmented views from the same instance. The benefit of optimizing the consistency loss is that the learned features for augmented views from the same instance are more compact and accordingly the classification loss optimization becomes easier, thus boosting the quality of the learned representations. This differs from InstDisc and MoCo that use an estimated prototype as the classifier weight to ease the optimization. Different from SimCLR that directly compares different instances, our approach does not require large batch size. Experimental results demonstrate competitive performance for linear evaluation and better performance than InstDisc, MoCo and SimCLR at downstream tasks, such as detection and segmentation, as well as competitive or superior performance compared to other methods with stronger training setting.
Learning Disentangled Representations for CounterFactual Regression
Authors: Negar Hassanpour, Russell Greiner

We consider the challenge of estimating treatment effects from observational data; and point out that, in general, only some factors based on the observed covariates X contribute to selection of the treatment T, and only some to determining the outcomes Y. We model this by considering three underlying sources of {X, T, Y} and show that explicitly modeling these sources offers great insight to guide designing models that better handle selection bias. This paper is an attempt to conceptualize this line of thought and provide a path to explore it further.
In this work, we propose an algorithm to (1) identify disentangled representations of the above-mentioned underlying factors from any given observational dataset D and (2) leverage this knowledge to reduce, as well as account for, the negative impact of selection bias on estimating the treatment effects from D. Our empirical results show that the proposed method achieves state-of-the-art performance in both individual and population based evaluation measures.