nils-herrmann/clean_scirepeval huggingface dataset:
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space
Authors: Fuxun Yu, Chenchen Liu, Yanzhi Wang, Xiang Chen

One popular hypothesis of neural network generalization is that the flat local minima of loss surface in parameter space leads to good generalization. However, we demonstrate that loss surface in parameter space has no obvious relationship with generalization, especially under adversarial settings. Through visualizing decision surfaces in both parameter space and input space, we instead show that the geometry property of decision surface in input space correlates well with the adversarial robustness. We then propose an adversarial robustness indicator, which can evaluate a neural network's intrinsic robustness property without testing its accuracy under adversarial attacks. Guided by it, we further propose our robust training method. Without involving adversarial training, our method could enhance network's intrinsic adversarial robustness against various adversarial attacks.
Neural representation and generation for RNA secondary structures
Authors: Zichao Yan, William L. Hamilton, Mathieu Blanchette

Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecule that can adopt complex structures which influence their cellular activities and functions. The design of large scale and complex biological structures spurs dedicated graph-based deep generative modeling techniques, which represents a key but underappreciated aspect of computational drug discovery. In this work, we investigate the principles behind representing and generating different RNA structural modalities, and propose a flexible framework to jointly embed and generate these molecular structures along with their sequence in a meaningful latent space. Equipped with a deep understanding of RNA molecular structures, our most sophisticated encoding and decoding methods operate on the molecular graph as well as the junction tree hierarchy, integrating strong inductive bias about RNA structural regularity and folding mechanism such that high structural validity, stability and diversity of generated RNAs are achieved. Also, we seek to adequately organize the latent space of RNA molecular embeddings with regard to the interaction with proteins, and targeted optimization is used to navigate in this latent space to search for desired novel RNA molecules.
Can a Fruit Fly Learn Word Embeddings?
Authors: Yuchen Liang, Chaitanya Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha, Mohammed J Zaki, Dmitry Krotov

The mushroom body of the fruit fly brain is one of the best studied systems in neuroscience. At its core it consists of a population of Kenyon cells, which receive inputs from multiple sensory modalities. These cells are inhibited by the anterior paired lateral neuron, thus creating a sparse high dimensional representation of the inputs. In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task. We show that this network can learn semantic representations of words and can generate both static and context-dependent word embeddings. Unlike conventional methods (e.g., BERT, GloVe) that use dense representations for word embedding, our algorithm encodes semantic meaning of words and their context in the form of sparse binary hash codes. The quality of the learned representations is evaluated on word similarity analysis, word-sense disambiguation, and document classification. It is shown that not only can the fruit fly network motif achieve performance comparable to existing methods in NLP, but, additionally, it uses only a fraction of the computational resources (shorter training time and smaller memory footprint).
Probabilistic Planning with Sequential Monte Carlo methods
Authors: Alexandre Piche, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, Chris Pal

In this work, we propose a novel formulation of planning which views it as a probabilistic inference problem over future optimal trajectories. This enables us to use sampling methods, and thus, tackle planning in continuous domains using a fixed computational budget.   We design a new algorithm,  Sequential Monte Carlo Planning, by leveraging classical methods in Sequential Monte Carlo and Bayesian smoothing in the context of control as inference. Furthermore, we show that Sequential Monte Carlo Planning can capture multimodal policies and can quickly learn continuous control tasks.
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?
Authors: Niv Giladi, Mor Shpigel Nacson, Elad Hoffer, Daniel Soudry

Recent developments have made it possible to accelerate neural networks training significantly using large batch sizes and data parallelism. Training in an asynchronous fashion, where delay occurs, can make training even more scalable. However, asynchronous training has its pitfalls, mainly a degradation in generalization, even after convergence of the algorithm. This gap remains not well understood, as theoretical analysis so far mainly focused on the convergence rate of asynchronous methods.
 We examine asynchronous training from the perspective of dynamical stability. We find that the degree of delay interacts with the learning rate, to change the set of minima accessible by an asynchronous stochastic gradient descent algorithm. We derive closed-form rules on how the learning rate could be changed, while keeping the accessible set the same. Specifically, for high delay values, we find that the learning rate should be kept inversely proportional to the delay. We then extend this analysis to include momentum. We find momentum should be either turned off, or modified to improve training stability.  We provide empirical experiments to validate our theoretical findings.
Analytically Tractable Bayesian Deep Q-Learning
Authors: Luong-Ha Nguyen, James-A. Goulet

Reinforcement learning (RL) has gained increasing interest since the demonstration it was able to reach human performance on video game benchmarks using deep Q-learning (DQN). The current consensus of DQN for training neural networks (NNs) on such complex environments is to rely on gradient-descent optimization (GD). This consensus ignores the uncertainty of the NN's parameters which is a key aspect for the selection of an optimal action given a state. Although alternative Bayesian deep learning methods exist, most of them still rely on GD and numerical approximations, and they typically do not scale on complex benchmarks such as the Atari game environment. In this paper, we present how we can adapt the temporal difference Q-learning framework to make it compatible with the tractable approximate Gaussian inference (TAGI) which allows estimating the posterior distribution of NN's parameters using a closed-form analytical method. Throughout the experiments with on- and off-policy reinforcement learning approaches, we demonstrate that TAGI can reach a performance comparable to backpropagation-trained networks while using only half the number of hyperparameters, and without relying on GD or numerical approximations.
DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
Authors: Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha, Sunghun Kim

Variational autoencoders (VAEs) have shown a promise in data-driven conversation modeling. However, most VAE conversation models match the approximate posterior distribution over the latent variables to a simple prior such as standard normal distribution, thereby restricting the generated responses to a relatively simple (e.g., single-modal) scope. In this paper, we propose DialogWAE, a conditional Wasserstein autoencoder (WAE) specially designed for dialogue modeling. Unlike VAEs that impose a simple distribution over the latent variables, DialogWAE models the distribution of data by training a GAN within the latent variable space. Specifically, our model samples from the prior and posterior distributions over the latent variables by transforming context-dependent random noise using neural networks and minimizes the Wasserstein distance between the two distributions. We further develop a Gaussian mixture prior network to enrich the latent space. Experiments on two popular datasets show that DialogWAE outperforms the state-of-the-art approaches in generating more coherent, informative and diverse responses.
ATTENTIVE EXPLAINABILITY FOR PATIENT TEMPORAL EMBEDDING
Authors: Daby Sow, Mohamed Ghalwash, Zach Shahn, Sanjoy Dey, Moulay Draidia, Li-wei Lehmann

Learning explainable patient temporal embeddings from observational data has mostly ignored the use of RNN architecture that excel in capturing temporal data dependencies but at the expense of explainability. This paper addresses this problem by introducing and applying an information theoretic approach to estimate the degree of explainability of such architectures. Using a communication paradigm, we formalize metrics of explainability by estimating the amount of information that an AI model needs to convey to a human end user to explain and rationalize its outputs. A key aspect of this work is to model human prior knowledge at the receiving end and measure the lack of explainability as a deviation from human prior knowledge. We apply this paradigm to medical concept representation problems by regularizing loss functions of temporal autoencoders according to the derived explainability metrics to guide the learning process towards models producing explainable outputs. We illustrate the approach with convincing experimental results for the generation of explainable temporal embeddings for critical care patient data.
Effect of top-down connections in Hierarchical Sparse Coding
Authors: Victor Boutin, Angelo Franciosini, Franck Ruffier, Laurent Perrinet

Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images. The simplest solution to solve this computationally hard problem is to decompose it into independent layerwise subproblems. However, neuroscientific evidence would suggest inter-connecting these subproblems as in the Predictive Coding (PC) theory, which adds top-down connections between consecutive layers. In this study, a new model called Sparse Deep Predictive Coding (SDPC) is introduced to assess the impact of this inter-layer feedback connection. In particular, the SDPC is compared with a Hierarchical Lasso (Hi-La) network made out of a sequence of Lasso layers. A 2-layered SDPC and a Hi-La networks are trained on 3 different databases and with different sparsity parameters on each layer. First, we show that the overall prediction error generated by SDPC is lower thanks to the feedback mechanism as it transfers prediction error between layers. Second, we demonstrate that the inference stage of the SDPC is faster to converge than for the Hi-La model. Third, we show that the SDPC also accelerates the learning process. Finally, the qualitative analysis of both models dictionaries, supported by their activation probability, show that the SDPC features are more generic and informative.
Optimizing Transformers with Approximate Computing for Faster, Smaller and more Accurate NLP Models
Authors: Amrit Nagarajan, Sanchari Sen, Jacob R. Stevens, Anand Raghunathan

Transformer models have garnered a lot of interest in recent years by delivering state-of-the-art performance in a range of Natural Language Processing (NLP) tasks. However, these models can have over a hundred billion parameters, presenting very high computational and memory requirements. We address this challenge through Approximate Computing, specifically targeting the use of Transformers in NLP tasks. Transformers are typically pre-trained and subsequently specialized for specific tasks through transfer learning. We observe that pre-trained Transformers are often over-parameterized for several downstream NLP tasks and propose a framework to create smaller and faster models with comparable accuracy. The key cornerstones of the framework are a Significance Analysis (SA) method to identify important components in a pre-trained Transformer for a given task, and techniques to approximate the less significant components. Our framework can be adapted to produce models that are faster, smaller and/or more accurate, depending on the user's constraints. We apply our framework to multiple Transformer models and different downstream tasks, including previously proposed optimized models like DistilBERT and Q8BERT. We demonstrate that our framework produces models that are up to 4$\times$ faster and up to 14$\times$ smaller (with less than 0.5% relative accuracy degradation), or up to 5.5% more accurate with simultaneous model size and speed improvements of up to 9.8$\times$ and 2.9$\times$, respectively.
Enhancing Cross-lingual Transfer by Manifold Mixup
Authors: Huiyun Yang, Huadong Chen, Hao Zhou, Lei Li

Based on large-scale pre-trained multilingual representations, recent cross-lingual transfer methods have achieved impressive transfer performances. However, the performance of target languages still lags far behind the source language. In this paper, our analyses indicate such a performance gap is strongly associated with the cross-lingual representation discrepancy. To achieve better cross-lingual transfer performance, we propose the cross-lingual manifold mixup (X-Mixup) method, which adaptively calibrates the representation discrepancy and gives a compromised representation for target languages. Experiments on the XTREME benchmark show X-Mixup achieves 1.8% performance gains on multiple text understanding tasks, compared with strong baselines, and significantly reduces the cross-lingual representation discrepancy.
Learning the Connections in Direct Feedback Alignment
Authors: Matthew Bailey Webster, Jonghyun Choi, changwook Ahn

Feedback alignment was proposed to address the biological implausibility of the backpropagation algorithm which requires the transportation of the weight transpose during the backwards pass. The idea was later built upon with the proposal of direct feedback alignment (DFA), which propagates the error directly from the output layer to each hidden layer in the backward path using a fixed random weight matrix. This contribution was significant because it allowed for the parallelization of the backwards pass by the use of these feedback connections. However, just as feedback alignment, DFA does not perform well in deep convolutional networks. We propose to learn the backward weight matrices in DFA, adopting the methodology of Kolen-Pollack learning, to improve training and inference accuracy in deep convolutional neural networks by updating the direct feedback connections such that they come to estimate the forward path. The proposed method improves the accuracy of learning by direct feedback connections and reduces the gap between parallel training to serial training by means of backpropagation.
Chameleon: Learning Model Initializations Across Tasks With Different Schemas
Authors: Lukas Brinkmeyer, Rafael Rego Drumond, Randolf Scholz, Josif Grabocka, Lars Schmidt-Thieme

Parametric models, and particularly neural networks, require weight initialization as a starting point for gradient-based optimization. Recent work shows that an initial parameter set can be learned from a population of supervised learning tasks that enables a fast convergence for unseen tasks even when only a handful of instances is available (model-agnostic meta-learning). 
Currently, methods for learning model initializations are limited to a population of tasks sharing the same schema, i.e., the same number, order, type, and semantics of predictor and target variables.
In this paper, we address the problem of meta-learning weight initialization across tasks with different schemas, for example, if the number of predictors varies across tasks, while they still share some variables. We propose Chameleon, a model that learns to align different predictor schemas to a common representation. 
In experiments on 23 datasets of the OpenML-CC18 benchmark, we show that Chameleon can successfully learn parameter initializations across tasks with different schemas, presenting, to the best of our knowledge, the first cross-dataset few-shot classification approach for unstructured data.
Sensor Transformation Attention Networks
Authors: Stefan Braun, Daniel Neil, Enea Ceolini, Jithendar Anumula, Shih-Chii Liu

Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attentional mechanisms into neural networks increases the performance of the system substantially. We report on a new modular network architecture that applies an attentional mechanism not on temporal and spatial regions of the input, but on sensor selection for multi-sensor setups. This network called the sensor transformation attention network (STAN) is evaluated in scenarios which include the presence of natural noise or synthetic dynamic noise. We demonstrate how the attentional signal responds dynamically to changing noise levels and sensor-specific noise, leading to reduced word error rates (WERs) on both audio and visual tasks using TIDIGITS and GRID; and also on CHiME-3, a multi-microphone real-world noisy dataset. The improvement grows as more channels are corrupted as demonstrated on the CHiME-3 dataset. Moreover, the proposed STAN architecture naturally introduces a number of advantages including ease of removing sensors from existing architectures, attentional interpretability, and increased robustness to a variety of noise environments.
Improving Policy Gradient by Exploring Under-appreciated Rewards
Authors: Ofir Nachum, Mohammad Norouzi, Dale Schuurmans

This paper presents a novel form of policy gradient for model-free reinforcement learning (RL) with improved exploration properties. Current policy-based methods use entropy regularization to encourage undirected exploration of the reward landscape, which is ineffective in high dimensional spaces with sparse rewards. We propose a more directed exploration strategy that promotes exploration of under-appreciated reward regions. An action sequence is considered under-appreciated if its log-probability under the current policy under-estimates its resulting reward. The proposed exploration strategy is easy to implement, requiring only small modifications to the standard REINFORCE algorithm. We evaluate the approach on a set of algorithmic tasks that have long challenged RL methods. We find that our approach reduces hyper-parameter sensitivity and demonstrates significant improvements over baseline methods. Notably, the approach is able to solve a benchmark multi-digit addition task. To our knowledge, this is the first time that a pure RL method has solved addition using only reward feedback.
Online Limited Memory Neural-Linear Bandits
Authors: Tom Zahavy, Ofir Nabati, Leor Cohen, Shie Mannor

We study neural-linear bandits for solving problems where both exploration and representation learning play an important role. Neural-linear bandits leverage the representation power of deep neural networks and combine it with efficient exploration mechanisms, designed for linear contextual bandits, on top of the last hidden layer. Since the representation is optimized during learning, information regarding exploration with “old” features is lost. We propose the first limited memory neural- linear bandit that is resilient to this catastrophic forgetting phenomenon by solving a semi-definite program. We then approximate the semi-definite program using stochastic gradient descent to make the algorithm practical and adjusted for online usage. We perform simulations on a variety of data sets, including regression, classification, and sentiment analysis. In addition, we evaluate our algorithm in a challenging uplink rate-control application. The bandit controls the transmission rates of data segments over cellular links to achieve optimal throughput. We observe that our algorithm achieves superior performance and shows resilience to catastrophic forgetting.
Adversary A3C for Robust Reinforcement Learning
Authors: Zhaoyuan Gu, Zhenzhong Jia, Howie Choset

Asynchronous Advantage Actor Critic (A3C) is an effective Reinforcement Learning (RL) algorithm for a wide range of tasks, such as Atari games and robot control. The agent learns policies and value function through trial-and-error interactions with the environment until converging to an optimal policy. Robustness and stability are critical in RL; however, neural network can be vulnerable to noise from unexpected sources and is not likely to withstand very slight disturbances. We note that agents generated from mild environment using A3C are not able to handle challenging environments. Learning from adversarial examples, we proposed an algorithm called Adversary Robust A3C (AR-A3C) to improve the agent’s performance under noisy environments. In this algorithm, an adversarial agent is introduced to the learning process to make it more robust against adversarial disturbances, thereby making it more adaptive to noisy environments. Both simulations and real-world experiments are carried out to illustrate the stability of the proposed algorithm. The AR-A3C algorithm outperforms A3C in both clean and noisy environments.
On orthogonality and learning recurrent networks with long term dependencies
Authors: Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal

It is well known that it is challenging to train deep neural networks and recurrent neural networks for tasks that exhibit long term dependencies. The vanishing or exploding gradient problem is a well known issue associated with these challenges. One approach to addressing vanishing and exploding gradients is to use either soft or hard constraints on weight matrices so as to encourage or enforce orthogonality. Orthogonal matrices preserve gradient norm during backpropagation and can therefore be a desirable property; however, we find that hard constraints on orthogonality can negatively affect the speed of convergence and model performance. This paper explores the issues of optimization convergence, speed and gradient stability using a variety of different methods for encouraging or enforcing orthogonality. In particular we propose a weight matrix factorization and parameterization strategy through which we we can bound matrix norms and therein control the degree of expansivity induced during backpropagation.
ABSTRACTING INFLUENCE PATHS  FOR EXPLAINING (CONTEXTUALIZATION OF) BERT MODELS
Authors: Kaiji Lu, Zifan Wang, Piotr Mardziel, Anupam Datta

While “attention is all you need” may be proving true, we do not yet know why: attention-based transformer models such as BERT are superior but how they contextualize information even for simple grammatical rules such as subject-verb number agreement(SVA) is uncertain. We introduce multi-partite patterns, abstractions of sets of paths through a neural network model. Patterns quantify and localize the effect of an input concept (e.g., a subject’s number) on an output concept (e.g. corresponding verb’s number) to paths passing through a sequence of model components, thus surfacing how BERT contextualizes information. We describe guided pattern refinement, an efficient search procedure for finding sufficient and sparse patterns representative of concept-critical paths. We discover that patterns generate succinct and meaningful explanations for BERT, highlighted by “copy” and “transfer” operations implemented by skip connections and attention heads, respectively. We also show how pattern visualizations help us understand how BERT contextualizes various grammatical concepts, such as SVA across clauses, and why it makes errors in some cases while succeeding in others.