nils-herrmann/clean_scirepeval huggingface dataset:
HIPPOCAMPAL NEURONAL REPRESENTATIONS IN CONTINUAL LEARNING
Authors: Samia Mohinta, Rui Ponte Costa, Stephane Ciocchi

The hippocampus has long been associated with spatial memory and goal-directed
spatial navigation. However, the region’s independent role in continual learning of
navigational strategies has seldom been investigated. Here we analyse populationlevel
activity of hippocampal CA1 neurons in the context of continual learning of
two different spatial navigation strategies. Demixed Principal Component Analysis
(dPCA) is applied on neuronal recordings from 612 hippocampal CA1 neurons
of rodents learning to perform allocentric and egocentric spatial tasks. The components
uncovered using dPCA from the firing activity reveal that hippocampal
neurons encode relevant task variables such decisions, navigational strategies and
reward location. We compare this hippocampal features with standard reinforcement
learning algorithms, highlighting similarities and differences. Finally, we
demonstrate that a standard deep reinforcement learning model achieves similar
average performance when compared to animal learning, but fails to mimic animals
during task switching. Overall, our results gives insights into how the hippocampus
solves reinforced spatial continual learning, and puts forward a framework
to explicitly compare biological and machine learning during spatial continual
learning.
Learning Deep Generative Models of Graphs
Authors: Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia

Graphs are fundamental data structures required to model many important real-world data, from knowledge graphs, physical and social interactions to molecules and proteins. In this paper, we study the problem of learning generative models of graphs from a dataset of graphs of interest. After learning, these models can be used to generate samples with similar properties as the ones in the dataset.  Such models can be useful in a lot of applications, e.g. drug discovery and knowledge graph construction. The task of learning generative models of graphs, however, has its unique challenges. In particular, how to handle symmetries in graphs and ordering of its elements during the generation process are important issues. We propose a generic graph neural net based model that is capable of generating any arbitrary graph.  We study its performance on a few graph generation tasks compared to baselines that exploit domain knowledge.  We discuss potential issues and open problems for such generative models going forward.
The Neural Noisy Channel
Authors: Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomas Kocisky

We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control how much of the conditioning sequence the channel model needs to read in order to generate a subsequent symbol, we obtain a tractable and effective beam search decoder. Experimental results on abstractive sentence summarisation, morphological inflection, and machine translation show that noisy channel models outperform direct models, and that they significantly benefit from increased amounts of unpaired output data that direct models cannot easily use.
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space
Authors: Shihao Zhao, Xingjun Ma, Yisen Wang, James Bailey, Bo Li, Yu-Gang Jiang

Deep neural networks (DNNs) have been widely adopted in different applications to achieve state-of-the-art performance. However, they are often applied as a black box with limited understanding of what the model has learned from the data. In this paper, we focus on image classification and propose a method to visualize and understand the class-wise patterns learned by DNNs trained under three different settings including natural, backdoored and adversarial. Different from existing class-wise deep representation visualizations, our method searches for a single predictive pattern in the input (i.e. pixel) space for each class. Based on the proposed method, we show that DNNs trained on natural (clean) data learn abstract shapes along with some texture, and backdoored models learn a small but highly predictive pattern for the backdoor target class. Interestingly, the existence of class-wise predictive patterns in the input space indicates that even DNNs trained on clean data can have backdoors, and the class-wise patterns identified by our method can be readily applied to “backdoor” attack the model. In the adversarial setting, we show that adversarially trained models learn more simplified shape patterns. Our method can serve as a useful tool to better understand DNNs trained on different datasets under different settings.
Exploiting Class Activation Value for Partial-Label Learning
Authors: Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama

Partial-label learning (PLL) solves the multi-class classification problem, where each training instance is assigned a set of candidate labels that include the true label. Recent advances showed that PLL can be compatible with deep neural networks, which achieved state-of-the-art performance. However, most of the existing deep PLL methods focus on designing proper training objectives under various assumptions on the collected data, which may limit their performance when the collected data cannot satisfy the adopted assumptions. In this paper, we propose to exploit the learned intrinsic representation of the model to identify the true label in the training process, which does not rely on any assumptions on the collected data. We make two key contributions. As the first contribution, we empirically show that the class activation map (CAM), a simple technique for discriminating the learning patterns of each class in images, could surprisingly be utilized to make accurate predictions on selecting the true label from candidate labels. Unfortunately, as CAM is confined to image inputs with convolutional neural networks, we are yet unable to directly leverage CAM to address the PLL problem with general inputs and models. Thus, as the second contribution, we propose the class activation value (CAV), which owns similar properties of CAM, while CAV is versatile in various types of inputs and models. Building upon CAV, we propose a novel method named CAV Learning (CAVL) that selects the true label by the class with the maximum CAV for model training. Extensive experiments on various datasets demonstrate that our proposed CAVL method achieves state-of-the-art performance.
Meta-Learning Probabilistic Inference for Prediction
Authors: Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard Turner

This paper introduces a new framework for data efficient and versatile learning. 
1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 
2) We introduce \Versa{}, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. \Versa{} substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training.
3) We evaluate \Versa{} on benchmark datasets where the method sets new state-of-the-art results, and can handle arbitrary number of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.
Learning to Rank Learning Curves
Authors: Martin Wistuba, Tejaswini Pedapati

Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to existing methods, we consider this task as a ranking and transfer learning problem. We qualitatively show that by optimizing a pairwise ranking loss and leveraging learning curves from other data sets, our model is able to effectively rank learning curves without having to observe many or very long learning curves. We further demonstrate that our method can be used to accelerate a neural architecture search by a factor of up to 100 without a significant performance degradation of the discovered architecture. In further experiments we analyze the quality of ranking, the influence of different model components as well as the predictive behavior of the model.
FED-$\chi^2$: Secure Federated Correlation Test
Authors: Lun Wang, Qi Pang, Shuai Wang, Dawn Song

In this paper, we propose the first secure federated $\chi^2$-test protocol, FED-$\chi^2$. We recast $\chi^2$-test as a problem of the second moment estimation and use stable projection to encode the local information in a short vector. Due to the fact that such encodings can be aggregated with summation, secure aggregation can smoothly be applied to conceal the individual updates. We formally establish the security guarantee of FED-$\chi^2$ by demonstrating that the joint distribution is hidden in a subspace containing exponentially possible distributions. Our evaluation results show that FED-$\chi^2$ achieves good accuracy with small client-side computation overhead. FED-$\chi^2$ performs comparably to the centralized $\chi^2$-test in several real-world case studies. The code for evaluation is in the supplementary material.
Language Modeling with Graph Temporal Convolutional Networks
Authors: Hongyin Luo, Yichen Li, Jie Fu, James Glass

Recently, there have been some attempts to use non-recurrent neural models for language modeling. 
However, a noticeable performance gap still remains. 
We propose a non-recurrent neural language model, dubbed graph temporal convolutional network (GTCN), that relies on graph neural network blocks and convolution operations. While the standard recurrent neural network language models encode sentences sequentially without modeling higher-level structural information, our model regards sentences as graphs and processes input words within a message propagation framework, aiming to learn better syntactic information by inferring skip-word connections. Specifically, the graph network blocks operate in parallel and learn the underlying graph structures in sentences without any additional annotation pertaining to structure knowledge. Experiments demonstrate that the model without recurrence can achieve comparable perplexity results in language modeling tasks and successfully learn syntactic information.
SupportNet: solving catastrophic forgetting in class incremental learning with support data
Authors: Yu Li, Zhongxiao Li, Lizhong Ding, Yijie Pan, Chao Huang, Yuhui Hu, Wei Chen, Xin Gao

A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic forgetting. Here we propose a novel method, SupportNet, to efficiently and effectively solve the catastrophic forgetting problem in the class incremental learning scenario. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to stabilize the learned representation and ensure the robustness of the learned model. We validate our method with comprehensive experiments on various tasks, which show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and even reaches similar performance as the deep learning model trained from scratch on both old and new data.
FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE
Authors: Masanori Yamada, Kim Heecheol, Kosuke Miyoshi, Hiroshi Yamakawa

A state-of-the-art generative model, a ”factorized action variational autoencoder (FAVAE),” is presented for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision. The purpose of disentangled representation learning is to obtain interpretable and transferable representations from data. We focused on the disentangled representation of sequential data because there is a wide range of potential applications if disentanglement representation is extended to sequential data such as video, speech, and stock price data. Sequential data is characterized by dynamic factors and static factors: dynamic factors are time-dependent, and static factors are independent of time. Previous works succeed in disentangling static factors and dynamic factors by explicitly modeling the priors of latent variables to distinguish between static and dynamic factors. However, this model can not disentangle representations between dynamic factors, such as disentangling ”picking” and ”throwing” in robotic tasks. In this paper, we propose new model that can disentangle multiple dynamic factors. Since our method does not require modeling priors, it is capable of disentangling ”between” dynamic factors. In experiments, we show that FAVAE can extract the disentangled dynamic factors.
Benefits of Assistance over Reward Learning
Authors: Rohin Shah, Pedro Freire, Neel Alex, Rachel Freedman, Dmitrii Krasheninnikov, Lawrence Chan, Michael D Dennis, Pieter Abbeel, Anca Dragan, Stuart Russell

Much recent work has focused on how an agent can learn what to do from human feedback, leading to two major paradigms. The first paradigm is reward learning, in which the agent learns a reward model through human feedback that is provided externally from the environment. The second is assistance, in which the human is modeled as a part of the environment, and the true reward function is modeled as a latent variable in the environment that the agent may make inferences about. The key difference between the two paradigms is that in the reward learning paradigm, by construction there is a separation between reward learning and control using the learned reward. In contrast, in assistance these functions are performed as needed by a single policy. By merging reward learning and control, assistive agents can reason about the impact of control actions on reward learning, leading to several advantages over agents based on reward learning. We illustrate these advantages in simple environments by showing desirable qualitative behaviors of assistive agents that cannot be found by agents based on reward learning.
A Frequency Perspective of Adversarial Robustness
Authors: Shishira Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava

Adversarial examples pose a unique challenge for deep learning systems.  Despite recent advances in both attacks and defenses, there is still a lack of clarity and consensus in the community about the true nature and underlying properties of adversarial examples. A deep understanding of these examples can provide new insights towards the development of more effective attacks and defenses. Driven by the common misconception that adversarial examples are high-frequency noise, we present a frequency-based understanding of adversarial examples, supported by theoretical and empirical findings. Our analysis shows that adversarial examples are neither in high-frequency nor in low-frequency components, but are simply dataset dependent. Particularly, we highlight the glaring disparities between models trained on CIFAR-10 and ImageNet-derived datasets. Utilizing this framework, we analyze many intriguing properties of training robust models with frequency constraints, and propose a frequency-based explanation for the commonly observed accuracy vs. robustness trade-off.
Asymptotics of Wide Networks from Feynman Diagrams
Authors: Ethan Dyer, Guy Gur-Ari

Understanding the asymptotic behavior of wide networks is of considerable interest. In this work, we present a general method for analyzing this large width behavior. The method is an adaptation of Feynman diagrams, a standard tool for computing multivariate Gaussian integrals. We apply our method to study training dynamics, improving existing bounds and deriving new results on wide network evolution during stochastic gradient descent. Going beyond the strict large width limit, we present closed-form expressions for higher-order terms governing wide network training, and test these predictions empirically.
$\mathrm{SO}(2)$-Equivariant Reinforcement Learning
Authors: Dian Wang, Robin Walters, Robert Platt

Equivariant neural networks enforce symmetry within the structure of their convolutional layers, resulting in a substantial improvement in sample efficiency when learning an equivariant or invariant function. Such models are applicable to robotic manipulation learning which can often be formulated as a rotationally symmetric problem. This paper studies equivariant model architectures in the context of $Q$-learning and actor-critic reinforcement learning. We identify equivariant and invariant characteristics of the optimal $Q$-function and the optimal policy and propose equivariant DQN and SAC algorithms that leverage this structure. We present experiments that demonstrate that our equivariant versions of DQN and SAC can be significantly more sample efficient than competing algorithms on an important class of robotic manipulation problems.
Machine Translation With Weakly Paired Bilingual Documents
Authors: Lijun Wu, Jinhua Zhu, Di He, Fei Gao, Xu Tan, Tao Qin, Tie-Yan Liu

Neural machine translation, which achieves near human-level performance in some languages, strongly relies on the availability of large amounts of parallel sentences, which hinders its applicability to low-resource language pairs. Recent works explore the possibility of unsupervised machine translation with monolingual data only, leading to much lower accuracy compared with the supervised one. Observing that weakly paired bilingual documents are much easier to collect than bilingual sentences, e.g., from Wikipedia, news websites or books, in this paper, we investigate the training of translation models with weakly paired bilingual documents. Our approach contains two components/steps. First, we provide a simple approach to mine implicitly bilingual sentence pairs from document pairs which can then be used as supervised signals for training. Second, we leverage the topic consistency of two weakly paired documents and learn the sentence-to-sentence translation by constraining the word distribution-level alignments.  We evaluate our proposed method on weakly paired documents from Wikipedia on four tasks, the widely used WMT16 German$\leftrightarrow$English and WMT13 Spanish$\leftrightarrow$English tasks, and obtain $24.1$/$30.3$ and $28.0$/$27.6$ BLEU points separately, outperforming
state-of-the-art unsupervised results by more than 5 BLEU points and reducing the gap between unsupervised translation and supervised translation up to 50\%.
Exploiting structured data for learning contagious diseases under incomplete testing
Authors: Maggie Makar, Lauren West, David Hooper, Eric Horvitz, Erica Shenoy, John Guttag

One of the ways that machine learning algorithms can help control the spread of an infectious disease is by building models that predict who is likely to get infected whether or not they display any symptoms, making them good candidates for preemptive isolation. In this work we ask: can we build reliable infection prediction models when the observed data is collected under limited, and biased testing that prioritizes testing symptomatic individuals? Our analysis suggests that under favorable conditions, incomplete testing might be sufficient to achieve relatively good out-of-sample prediction error. Favorable conditions occur when untested-infected individuals have sufficiently different characteristics from untested-healthy, and when the infected individuals are "potent", meaning they infect a large majority of their neighbors. We develop an algorithm that predicts infections, and show that it outperforms benchmarks on simulated data. We apply our model to data from a large hospital to predict Clostridioides difficile infections; a communicable disease that is characterized by asymptomatic (i.e., untested) carriers. Using a proxy instead of the unobserved untested-infected state, we show that our model outperforms benchmarks in predicting infections.
Stochastic Canonical Correlation Analysis: A Riemannian Approach
Authors: Zihang Meng, Rudrasis Chakraborty, Vikas Singh

We present an efficient stochastic algorithm (RSG+) for canonical correlation analysis (CCA) derived via a differential geometric perspective of the underlying optimization task. We show that exploiting the Riemannian structure of the problem reveals natural strategies for modified forms of manifold stochastic gradient descent schemes that have been variously used in the literature for numerical optimization on manifolds. Our developments complement existing methods for this problem which either require $O(d^3)$ time complexity per iteration with $O(\frac{1}{\sqrt{t}})$ convergence rate (where $d$ is the dimensionality) or only extract the top $1$ component with $O(\frac{1}{t})$ convergence rate. In contrast, our algorithm achieves $O(d^2k)$ runtime complexity per iteration for extracting top $k$ canonical components with $O(\frac{1}{t})$ convergence rate. We present our theoretical analysis as well as experiments describing the empirical behavior of our algorithm, including a potential application of this idea for training fair models where the label of protected attribute is missing or otherwise unavailable.
Losing Less: A Loss for Differentially Private Deep Learning
Authors: Ali Shahin Shamsabadi, Nicolas Papernot

Differentially Private Stochastic Gradient Descent, DP-SGD, is the canonical approach to training deep neural networks with guarantees of Differential Privacy (DP). However, the modifications DP-SGD introduces to vanilla gradient descent negatively impact the accuracy of deep neural networks. In this paper, we are the first to observe that some of this performance can be recovered when training with a loss tailored to DP-SGD; we challenge cross-entropy as the de facto loss for deep learning with DP. Specifically, we introduce a loss combining three terms: the summed squared error, the focal loss, and a regularization penalty. The first term encourages learning with faster convergence. The second term emphasizes hard-to-learn examples in the later stages of training. Both are beneficial because the privacy cost of learning increases with every  step of DP-SGD. The third term helps control the sensitivity of learning, decreasing the bias introduced by gradient clipping in DP-SGD. Using our loss function, we achieve new state-of-the-art tradeoffs between privacy and accuracy on MNIST, FashionMNIST, and CIFAR10. Most importantly, we improve the accuracy of DP-SGD on CIFAR10 by $4\%$ for a DP guarantee of $\varepsilon=3$.