nils-herrmann/clean_scirepeval huggingface dataset:
Dynamical System Embedding for Efficient Intrinsically Motivated Artificial Agents
Authors: Ruihan Zhao, Stas Tiomkin, Pieter Abbeel

Mutual Information between agent Actions and environment States (MIAS) quantifies the influence of agent on its environment. Recently, it was found that intrinsic motivation in artificial agents emerges from the maximization of MIAS.
For example, empowerment is an information-theoretic approach to intrinsic motivation, which has been shown to solve a broad range of standard RL benchmark problems. The estimation of empowerment for arbitrary dynamics is a challenging problem because it relies on the estimation of MIAS. Existing approaches rely on sampling, which have formal limitations, requiring exponentially many samples. In this work, we develop a novel approach for the estimation of empowerment in unknown arbitrary dynamics from visual stimulus only, without sampling for the estimation of MIAS. The core idea is to represent the relation between action sequences and future states by a stochastic dynamical system in latent space, which admits an efficient estimation of MIAS by the ``Water-Filling" algorithm from information theory. We construct this embedding with deep neural networks trained on a novel objective function and demonstrate our approach by numerical simulations of non-linear continuous-time dynamical systems. We show that the designed embedding preserves information-theoretic properties of the original dynamics, and enables us to solve the standard AI benchmark problems.
UWGAN: UNDERWATER GAN FOR REAL-WORLD UNDERWATER COLOR RESTORATION AND DEHAZING
Authors: Nan Wang, Yabin Zhou, Fenglei Han, Lichao Wan, Haitao Zhu, Yaojing Zheng

In real-world underwater environment, exploration of seabed resources, underwater archaeology, and underwater fishing rely on a variety of sensors, vision sensor is the most important one due to its high information content, non-intrusive, and passive nature. However, wavelength-dependent light attenuation and back-scattering result in color distortion and haze effect, which degrade the visibility of images. To address this problem, firstly, we proposed an unsupervised generative adversarial network (GAN) for generating realistic underwater images (color distortion and haze effect simulation) from in-air image and depth map pairs. Secondly, U-Net, which is trained efficiently using synthetic underwater dataset, is adopted for color restoration and de-hazing. Our model directly reconstructs underwater clear images using end-to-end autoencoder networks, while maintaining scene content structural similarity. The results obtained by our method were compared with existing methods qualitatively and quantitatively. Experimental results on open real-world underwater datasets demonstrate that the presented method performs well on different actual underwater scenes, and the processing speed can reach up to 125FPS on images running on one NVIDIA 1060 GPU.
Computing committor functions for the study of rare events using deep learning with importance sampling
Authors: Qianxiao Li, Bo Lin, Weiqing Ren

The committor function is a central object of study in understanding transitions between metastable states in complex systems. However, computing the committor function for realistic systems at low temperatures is a challenging task, due to the curse of dimensionality and the scarcity of transition data. In this paper, we introduce a computational approach that overcomes these issues and achieves good performance on complex benchmark problems with rough energy landscapes. The new approach combines deep learning, importance sampling and feature engineering techniques. This establishes an alternative practical method for studying rare transition events among metastable states of complex, high dimensional systems.
Dual Learning: Theoretical Study and Algorithmic Extensions
Authors: Zhibing Zhao, Yingce Xia, Tao Qin, Tie-Yan Liu

Dual learning has been successfully applied in many machine learning applications, including machine translation, image-to-image  transformation, etc. The high-level idea of dual learning is very intuitive: if we map an x from one domain to another and then map it back, we should recover the original x. Although its effectiveness has been empirically verified, theoretical understanding of dual learning is still missing. In this paper, we conduct a theoretical study to understand why and when dual learning can improve a mapping function. Based on the theoretical discoveries, we extend dual learning by introducing more related mappings and propose highly symmetric frameworks, cycle dual learning and multipath dual learning, in both of which we can leverage the feedback signals from additional domains to improve the qualities of the mappings. We prove that both cycle dual learning and multipath dual learning can boost the performance of standard dual learning under mild conditions. Experiments on WMT 14 English↔German and MultiUN English↔French translations verify our theoretical findings on dual learning, and the results on the translations among English, French, and Spanish of MultiUN demonstrate the efficacy of cycle dual learning and multipath dual learning.
Representing dynamically: An active process for describing sequential data
Authors: Juan Sebastian Olier, Emilia Barakova, Matthias Rauterberg, Carlo Regazzoni

We propose an unsupervised method for building dynamic representations of sequential data, particularly of observed interactions. The method simultaneously acquires representations of input data and its dynamics. It is based on a hierarchical generative model composed of two levels. In the first level, a model learns representations to generate observed data. In the second level, representational states encode the dynamics of the lower one. The model is designed as a Bayesian network with switching variables represented in the higher level, and which generates transition models. The method actively explores the latent space guided by its knowledge and the uncertainty about it. That is achieved by updating the latent variables from prediction error signals backpropagated to the latent space. So, no encoder or inference models are used since the generators also serve as their inverse transformations.
The method is evaluated in two scenarios, with static images and with videos. The results show that the adaptation over time leads to better performance than with similar architectures without temporal dependencies, e.g., variational autoencoders. With videos, it is shown that the system extracts the dynamics of the data in states that highly correlate with the ground truth of the actions observed.
Deep Convolution for Irregularly Sampled Temporal Point Clouds
Authors: Erich Merrill III, Stefan Lee, Li Fuxin, Thomas G Dietterich, Alan Fern

We consider the problem of modeling the dynamics of continuous spatial-temporal processes represented by irregular samples through both space and time. Such processes occur in sensor networks, citizen science, multi-robot systems, and many others. 
We propose a new deep model that is able to directly learn and predict over this irregularly sampled data, without voxelization, by leveraging a recent convolutional architecture for static point clouds. The model also easily incorporates the notion of multiple entities in the process. In particular, the model can flexibly answer prediction queries about arbitrary space-time points for different entities regardless of the distribution of the training or test-time data. We present experiments on real-world weather station data and battles between large armies in StarCraft II. The results demonstrate the model's flexibility in answering a variety of query types and demonstrate improved performance and efficiency compared to state-of-the-art baselines.
DIME: AN INFORMATION-THEORETIC DIFFICULTY MEASURE FOR AI DATASETS
Authors: Peiliang Zhang, Huan Wang, Nikhil Naik, Caiming Xiong, Richard Socher

Evaluating the relative difficulty of widely-used benchmark datasets across time and across data modalities is important for accurately measuring progress in machine learning.  To help tackle this problem, we proposeDIME, an information-theoretic DIfficulty MEasure for datasets, based on conditional entropy estimation of the sample-label distribution.  Theoretically,  we prove a model-agnostic and modality-agnostic lower bound on the 0-1 error by extending Fano’s inequality to the common supervised learning scenario where labels are discrete and features are continuous. Empirically, we estimate this lower bound using a neural network to compute DIME. DIME can be decomposed into components attributable to the data distribution and the number of samples.  DIME can also compute per-class difficulty scores. Through extensive experiments on both vision and language datasets, we show that DIME is well-aligned with empirically observed performance of state-of-the-art machine learning models. We hope that DIME can aid future dataset design and model-training strategies.
Rapid Task-Solving in Novel Environments
Authors: Samuel Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matthew Botvinick, David Raposo

We propose the challenge of rapid task-solving in novel environments (RTS), wherein an agent must solve a series of tasks as rapidly as possible in an unfamiliar environment. An effective RTS agent must balance between exploring the unfamiliar environment and solving its current task, all while building a model of the new environment over which it can plan when faced with later tasks. While modern deep RL agents exhibit some of these abilities in isolation, none are suitable for the full RTS challenge. To enable progress toward RTS, we introduce two challenge domains: (1) a minimal RTS challenge called the Memory&Planning Game and (2) One-Shot StreetLearn Navigation, which introduces scale and complexity from real-world data. We demonstrate that state-of-the-art deep RL agents fail at RTS in both domains, and that this failure is due to an inability to plan over gathered knowledge. We develop Episodic Planning Networks (EPNs) and show that deep-RL agents with EPNs excel at RTS, outperforming the nearest baseline by factors of 2-3 and learning to navigate held-out StreetLearn maps within a single episode. We show that EPNs learn to execute a value iteration-like planning algorithm and that they generalize to situations beyond their training experience.
WHY FLATNESS DOES AND DOES NOT CORRELATE WITH GENERALIZATION FOR DEEP NEURAL NETWORKS 
Authors: Shuofeng Zhang, Isaac Reid, Guillermo Valle-Perez, Ard A. Louis

The intuition that local flatness of the loss landscape is correlated with better generalization for deep neural networks (DNNs) has been explored for decades, spawning many different flatness measures.  Recently, this link with generalization has been called into question by a demonstration that many measures of flatness are vulnerable to parameter re-scaling which arbitrarily changes their value without changing neural network outputs.  Here we show that, in addition,  some popular variants of SGD such as Adam and Entropy-SGD, can also break the flatness-generalization correlation.  As an alternative to flatness measures,  we use a function based picture and propose using the log of  Bayesian prior upon initialization, $\log P(f)$, as a predictor of the generalization when a DNN converges on function $f$ after training to zero error.   The prior is directly proportional to the Bayesian posterior for functions that give zero error on a test set. For the case of image classification, we show that $\log P(f)$ is a significantly more robust predictor of generalization than flatness measures are. Whilst local flatness measures fail under parameter re-scaling, the prior/posterior, which is global quantity,  remains invariant under re-scaling. Moreover, the correlation with generalization as a function of data complexity remains good for different variants of SGD.
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
Authors: Cong Guo, Yuxian Qiu, Jingwen Leng, Xiaotian Gao, Chen Zhang, Yunxin Liu, Fan Yang, Yuhao Zhu, Minyi Guo

Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the original datasets under privacy-sensitive and confidential scenarios. However, current DFQ solutions degrade accuracy, need synthetic data to calibrate networks, and are time-consuming and costly. This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with low computation and memory requirements. With the theoretical analysis of the second-order information of DNN task loss, we decompose and approximate the Hessian-based optimization objective into three diagonal sub-items, which have different areas corresponding to three dimensions of weight tensor: element-wise, kernel-wise, and output channel-wise. Then, we progressively compose sub-items and propose a novel data-free optimization objective in the discrete domain,  minimizing Constrained Absolute Sum of Error (or CASE in short), which surprisingly does not need any dataset and is even not aware of network architecture. We also design an efficient algorithm without back-propagation to further reduce the computation complexity of the objective solver. Finally, without fine-tuning and synthetic datasets, SQuant accelerates the data-free quantization process to a sub-second level with >30% accuracy improvement over the existing data-free post-training quantization works, with the evaluated models under 4-bit quantization. We have open-sourced the SQuant framework at https://github.com/clevercool/SQuant.
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation
Authors: Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh

Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional compilation heuristics, or very recently genetic algorithms and other stochastic methods. These methods suffer from frequent costly hardware measurements rendering them not only too time consuming but also suboptimal. As such, we devise a solution that can learn to quickly adapt to a previously unseen design space for code optimization, both accelerating the search and improving the output performance. This solution dubbed Chameleon leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain-knowledge inspired logic to improve the samples itself. Experimentation with real hardware shows that Chameleon provides 4.45x speed up in optimization time over AutoTVM, while also improving inference time of the modern deep networks by 5.6%.
Hierarchical Cross Contrastive Learning of Visual Representations
Authors: Hesen Chen, Ming Lin, Xiuyu Sun, Rong Jin

The rapid progress of self-supervised learning (SSL) has greatly reduced the labeling cost in computer vision. The key idea of SSL is to learn invariant visual representations by maximizing the similarity between different views of the same input image. In most SSL methods, the representation invariant is measured by a contrastive loss which compares one of the network outputs after the projection head to its augmented version. Albeit being effective, this approach overlooks the information containing in the hidden layer of the projection head therefore could be sub-optimal. In this work,  we propose a novel approach termed Hierarchical Cross Contrastive Learning(HCCL) to further distill the information mismatched by the conventional contrastive loss. The HCCL uses a hierarchical projection head to project the raw representations of the backbone into multiple latent spaces and then compares latent features across different levels and different views. By cross-level contrastive learning, HCCL not only regulates invariant on multiple hidden levels but also crosses different levels, improving the generalization ability of the learned visual representations. As a simple and generic method, HCCL can be applied to different SSL frameworks. We validate the efficacy of HCCL under classification, detection, segmentation, and few-shot learning tasks. Extensive experimental results show that HCCL outperforms most previous methods in various benchmark datasets.
Hierarchical Disentangle Network for Object Representation Learning
Authors: Shishi Qiao, Ruiping Wang, Shiguang Shan, Xilin Chen

An object can be described as the combination of primary visual attributes. Disentangling such underlying primitives is the long objective of representation learning. It is observed that categories have the natural multi-granularity or hierarchical characteristics, i.e. any two objects can share some common primitives in a particular category granularity while they may possess their unique ones in another granularity. However, previous works usually operate in a flat manner (i.e. in a particular granularity) to disentangle the representations of objects. Though they may obtain the primitives to constitute objects as the categories in that granularity, their results are obviously not efficient and complete. In this paper, we propose the hierarchical disentangle network (HDN) to exploit the rich hierarchical characteristics among categories to divide the disentangling process in a coarse-to-fine manner, such that each level only focuses on learning the specific representations in its granularity and finally the common and unique representations in all granularities jointly constitute the raw object. Specifically, HDN is designed based on an encoder-decoder architecture. To simultaneously ensure the disentanglement and interpretability of the encoded representations, a novel hierarchical generative adversarial network (GAN) is elaborately designed. Quantitative and qualitative evaluations on four object datasets validate the effectiveness of our method.
CoCon: A Self-Supervised Approach for Controlled Text Generation
Authors: Alvin Chan, Yew-Soon Ong, Bill Pung, Aston Zhang, Jie Fu

Pretrained Transformer-based language models (LMs) display remarkable natural language generation capabilities. With their immense potential, controlling text generation of such LMs is getting attention. While there are studies that seek to control high-level attributes (such as sentiment and topic) of generated text, there is still a lack of more precise control over its content at the word- and phrase-level. Here, we propose Content-Conditioner (CoCon) to control an LM's output text with a content input, at a fine-grained level. In our self-supervised approach, the CoCon block learns to help the LM complete a partially-observed text sequence by conditioning with content inputs that are withheld from the LM. Through experiments, we show that CoCon can naturally incorporate target content into generated texts and control high-level text attributes in a zero-shot manner.
Feudal Reinforcement Learning by Reading Manuals
Authors: Kai Wang, Zhonghao Wang, Mo Yu, Humphrey Shi

Reading to act is a prevalent but challenging task that requires the ability to follow a concise language instruction in an environment, with the help of textual knowledge about the environment. Previous works face the semantic mismatch between the low-level actions and the high-level language descriptions and require the human-designed curriculum to work properly. In this paper, we present a Feudal Reinforcement Learning (FRL) model consisting of a manager agent and a worker agent. The manager agent is a multi-hop planner, which deals with high-level abstract information and generates a series of sub-goals. The worker agent deals with the low-level perceptions and actions to achieve the sub-goals one by one. Our FRL framework effectively alleviates the mismatching between the text-level inference and the low-level perceptions and actions; and is general to various forms of environments, instructions and manuals. Our multi-hop planner contributes to the framework by further boosting the challenging tasks where multi-step reasoning from the texts is critical to achieving the instructed goals. We showcase our approach achieves competitive performance on two challenging tasks, Read to Fight Monsters (RTFM) and Messenger, without human-designed curriculum learning.
D-CODE: Discovering Closed-form ODEs from Observed Trajectories
Authors: Zhaozhi Qian, Krzysztof Kacprzyk, Mihaela van der Schaar

For centuries, scientists have manually designed closed-form ordinary differential equations (ODEs) to model dynamical systems. An automated tool to distill closed-form ODEs from observed trajectories would accelerate the modeling process. Traditionally, symbolic regression is used to uncover a closed-form prediction function $a=f(b)$ with label-feature pairs $(a_i, b_i)$ as training examples. However, an ODE models the time derivative $\dot{x}(t)$ of a dynamical system, e.g. $\dot{x}(t) = f(x(t),t)$, and the "label" $\dot{x}(t)$ is usually *not* observed. The existing ways to bridge this gap only perform well for a narrow range of settings with low measurement noise, frequent sampling, and non-chaotic dynamics. In this work, we propose the Discovery of Closed-form ODE framework (D-CODE), which advances symbolic regression beyond the paradigm of supervised learning. D-CODE leverages a novel objective function based on the variational formulation of ODEs to bypass the unobserved time derivative. For formal justification, we prove that this objective is a valid proxy for the estimation error of the true (but unknown) ODE. In the experiments, D-CODE successfully discovered the governing equations of a diverse range of dynamical systems under challenging measurement settings with high noise and infrequent sampling.
Robust Local Features for Improving the Generalization of Adversarial Training
Authors: Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang, John E. Hopcroft

Adversarial training has been demonstrated as one of the most effective methods for training robust models to defend against adversarial examples. However, adversarially trained models often lack adversarially robust generalization on unseen testing data. Recent works show that adversarially trained models are more biased towards global structure features. Instead, in this work, we would like to investigate the relationship between the generalization of adversarial training and the robust local features, as the robust local features generalize well for unseen shape variation. To learn the robust local features, we develop a Random Block Shuffle (RBS) transformation to break up the global structure features on normal adversarial examples. We continue to propose a new approach called Robust Local Features for Adversarial Training (RLFAT), which first learns the robust local features by adversarial training on the RBS-transformed adversarial examples, and then transfers the robust local features into the training of normal adversarial examples. To demonstrate the generality of our argument, we implement RLFAT in currently state-of-the-art adversarial training frameworks. Extensive experiments on STL-10, CIFAR-10 and CIFAR-100 show that RLFAT significantly improves both the adversarially robust generalization and the standard generalization of adversarial training. Additionally, we demonstrate that our models capture more local features of the object on the images, aligning better with human perception.