nils-herrmann/clean_scirepeval huggingface dataset:
Visual Hide and Seek
Authors: Boyuan Chen, Shuran Song, Hod Lipson, Carl Vondrick

We train embodied agents to play Visual Hide and Seek where a prey must navigate in a simulated environment in order to avoid capture from a predator. We place a variety of obstacles in the environment for the prey to hide behind, and we only give the agents partial observations of their environment using an egocentric perspective. Although we train the model to play this game from scratch without any prior knowledge of its visual world, experiments and visualizations show that a representation of other agents automatically emerges in the learned representation. Furthermore, we quantitatively analyze how agent weaknesses, such as slower speed, effect the learned policy. Our results suggest that, although agent weaknesses make the learning problem more challenging, they also cause useful features to emerge in the representation.
Log-DenseNet: How to Sparsify a DenseNet
Authors: Hanzhang Hu, Debadeepta Dey, Allie Del Giorno, Martial Hebert, J. Andrew Bagnell

Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency. In particular, the recent DenseNet is efficient in computation and parameters, and achieves state-of-the-art predictions by directly connecting each feature layer to all previous ones. However, DenseNet's extreme connectivity pattern may hinder its scalability to high depths, and in applications like fully convolutional networks, full DenseNet connections are prohibitively expensive. 
This work first experimentally shows that one key advantage of skip connections is to have short distances among feature layers during backpropagation. Specifically, using a fixed number of skip connections, the connection patterns with shorter backpropagation distance among layers have more accurate predictions. Following this insight, we propose a connection template, Log-DenseNet, which, in comparison to DenseNet,  only slightly increases the backpropagation distances among layers from 1 to  ($1 + \log_2 L$), but uses only $L\log_2 L$ total connections instead of $O(L^2)$. Hence, \logdenses are easier to scale than DenseNets, and no longer require careful GPU memory management. We demonstrate the effectiveness of our design principle by showing better performance than DenseNets on tabula rasa semantic segmentation, and competitive results on visual recognition.
GUIDEGAN:  ATTENTION  BASED  SPATIAL  GUIDANCE FOR  IMAGE-TO-IMAGE TRANSLATION
Authors: Yu Lin, Yigong Wang, Yifan Li, Zhuoyi Wang, Yang Gao, Latifur Khan

Recently, Generative Adversarial Network (GAN) and numbers of its variants have been widely used to solve the image-to-image translation problem and achieved extraordinary results in both a supervised and unsupervised manner. However, most GAN-based methods suffer from the imbalance problem between the generator and discriminator in practice. Namely, the relative model capacities of the generator and discriminator do not match, leading to mode collapse and/or diminished gradients. To tackle this problem, we propose a GuideGAN based on attention mechanism. More specifically, we arm the discriminator with an attention mechanism so not only it estimates the probability that its input is real, but also does it create an attention map that highlights the critical features for such prediction. This attention map then assists the generator to produce more plausible and realistic images. We extensively evaluate the proposed GuideGAN framework on a  number of image transfer tasks. Both qualitative results and quantitative comparison demonstrate the superiority of our proposed approach.
Training Interpretable Convolutional Neural Networks towards Class-specific Filters
Authors: Haoyu Liang, Zhihao Ouyang, Hang Su, Yuyuan Zeng, Zihao He, Shu-tao Xia, Jun Zhu, Bo Zhang

Convolutional neural networks (CNNs) have often been treated as “black-box” and successfully used in a range of tasks. However, CNNs still suffer from the problem of filter ambiguity – an intricate many-to-many mapping relationship between filters and features, which undermines the models’ interpretability. To interpret CNNs, most existing works attempt to interpret a pre-trained model, while neglecting to reduce the filter ambiguity hidden behind. To this end, we propose a simple but effective strategy for training interpretable CNNs. Specifically, we propose a novel Label Sensitive Gate (LSG) structure to enable the model to learn disentangled filters in a supervised manner, in which redundant channels experience a periodical shutdown as flowing through a learnable gate varying with input labels. To reduce redundant filters during training, LSG is constrained with a sparsity regularization. In this way, such training strategy imposes each filter’s attention to just one or few classes, namely class-specific. Extensive experiments demonstrate the fabulous performance of our method in generating sparse and highly label- related representation of the input. Moreover, comparing to the standard training strategy, our model displays less redundancy and stronger interpretability.
Information Regularized Neural Networks
Authors: Tianchen Zhao, Dejiao Zhang, Zeyu Sun, Honglak Lee

We formulate an information-based optimization problem for supervised classification. For invertible neural networks, the control of these information terms is passed down to the latent features and parameter matrix in the last fully connected layer, given that mutual information is invariant under invertible map.  We propose an objective function and prove that it solves the optimization problem. Our framework allows us to learn latent features in an more interpretable form while improving the classification performance. We perform extensive quantitative and qualitative experiments in comparison with the existing state-of-the-art classification models.
Probabilistic Meta-Learning for Bayesian Optimization
Authors: Felix Berkenkamp, Anna Eivazi, Lukas Grossberger, Kathrin Skubch, Jonathan Spitz, Christian Daniel, Stefan Falkner

Transfer and meta-learning algorithms leverage evaluations on related tasks in order to significantly speed up learning or optimization on a new problem. For applications that depend on uncertainty estimates, e.g., in Bayesian optimization, recent probabilistic approaches have shown good performance at test time, but either scale poorly with the number of data points or under-perform with little data on the test task. In this paper, we propose a novel approach to probabilistic transfer learning that uses a generative model for the underlying data distribution and simultaneously learns a latent feature distribution to represent unknown task properties. To enable fast and accurate inference at test-time, we introduce a novel meta-loss that structures the latent space to match the prior used for inference. Together, these contributions ensure that our probabilistic model exhibits high sample-efficiency and provides well-calibrated uncertainty estimates. We evaluate the proposed approach and compare its performance to probabilistic models from the literature on a set of Bayesian optimization transfer-learning tasks.
Neural spatio-temporal reasoning with object-centric self-supervised learning
Authors: David Ding, Felix Hill, Adam Santoro, Matthew Botvinick

Transformer-based language models have proved capable of rudimentary symbolic reasoning, underlining the effectiveness of  applying self-attention computations to sets of discrete entities. In this work, we apply this lesson to videos of physical interaction between objects. We show that self-attention-based models operating on discrete, learned, object-centric representations perform well on spatio-temporal reasoning tasks which were expressly designed to trouble traditional neural network models and to require higher-level cognitive processes such as causal reasoning and understanding of intuitive physics and narrative structure. We achieve state of the art results on two datasets, CLEVRER and CATER, significantly outperforming leading hybrid neuro-symbolic models. Moreover, we find that techniques from language modelling, such as BERT-style semi-supervised predictive losses, allow our model to surpass neuro-symbolic approaches while using 40% less labelled data. Our results corroborate the idea that neural networks can reason about the causal, dynamic structure of visual data and attain understanding of intuitive physics, which counters the popular claim that they are only effective at perceptual pattern-recognition and not reasoning per se.
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications
Authors: Yue Yang, Pengtao Xie

While deep learning methods have shown great success in medical image analysis, they require a number of medical images to train. Due to data privacy concerns and unavailability of medical annotators, it is oftentimes very difficult to obtain a lot of labeled medical images for model training. In this paper, we study cross-modality data augmentation to mitigate the data deficiency issue in medical imaging domain. We propose a discriminative unpaired image-to-image translation model which translate images in source modality into images in target modality where the translation task is conducted jointly with the downstream prediction task and the translation is guided by the prediction. Experiments on two applications demonstrate the effectiveness of our method.
Why Do Neural Response Generation Models Prefer Universal Replies?
Authors: Bowen Wu, Nan Jiang, Zhifeng Gao, Zongsheng Wang, Suke Li, Wenge Rong, Baoxun Wang

Recent advances in neural Sequence-to-Sequence (Seq2Seq) models reveal a purely data-driven approach to the response generation task. Despite its diverse variants and applications, the existing Seq2Seq models are prone to producing short and generic replies, which blocks such neural network architectures from being utilized in practical open-domain response generation tasks. In this research, we analyze this critical issue from the perspective of the optimization goal of models and the specific characteristics of human-to-human conversational corpora. Our analysis is conducted by decomposing the goal of Neural Response Generation (NRG) into the optimizations of word selection and ordering. It can be derived from the decomposing that Seq2Seq based NRG models naturally tend to select common words to compose responses, and ignore the semantic of queries in word ordering. On the basis of the analysis, we propose a max-marginal ranking regularization term to avoid Seq2Seq models from producing the generic and uninformative responses. The empirical experiments on benchmarks with several metrics have validated our analysis and proposed methodology.
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
Authors: Yang Zhang, Shiyu Chang, Mo Yu, Kaizhi Qian

There are two major paradigms of white-box adversarial attacks that attempt to impose input perturbations.  The first paradigm, called the fix-perturbation attack, crafts adversarial samples within a given perturbation level.  The second paradigm, called the zero-confidence attack, finds the smallest perturbation needed to cause misclassification, also known as the margin of an input feature.  While the former paradigm is well-resolved, the latter is not.  Existing zero-confidence attacks either introduce significant approximation errors, or are too time-consuming.  We therefore propose MarginAttack, a zero-confidence attack framework that is able to compute the margin with improved accuracy and efficiency.  Our experiments show that MarginAttack is able to compute a smaller margin than the state-of-the-art zero-confidence attacks, and matches the state-of-the-art fix-perturbation attacks.  In addition, it runs significantly faster than the Carlini-Wagner attack, currently the most accurate zero-confidence attack algorithm.
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles
Authors: Yichi Zhou, Shihong Song, Huishuai Zhang, Jun Zhu, Wei Chen, Tie-Yan Liu

In contextual bandit, one major challenge is to develop theoretically solid and empirically efficient  algorithms for general function classes. We present a novel algorithm called \emph{regularized optimism in face of uncertainty (ROFU)} for general contextual bandit problems. It exploits an optimization oracle to calculate the well-founded upper confidence bound (UCB).  Theoretically, for general function classes under very mild assumptions, it achieves a near-optimal regret bound $\Tilde{O}(\sqrt{T})$. Practically, one great advantage of ROFU is that the optimization oracle can be efficiently implemented with low computational cost. Thus, we can easily extend ROFU for  contextual bandits with deep neural networks as the function class, which outperforms strong baselines including the UCB and Thompson sampling variants.
Better Generalization by Efficient Trust Region Method
Authors: Xuanqing Liu, Jason D. Lee, Cho-Jui Hsieh

In this paper, we develop a trust region method for training deep neural networks. At each iteration, trust region method computes the search direction by solving a non-convex subproblem. Solving this subproblem is non-trivial---existing methods have only sub-linear convergence rate. In the first part, we show that a simple modification of gradient descent algorithm can converge to a global minimizer of the subproblem with an asymptotic linear convergence rate. Moreover, our method only requires Hessian-vector products, which can be computed efficiently by back-propagation in neural networks. In the second part, we apply our algorithm to train large-scale convolutional neural networks, such as VGG and MobileNets. Although trust region method is about 3 times slower than SGD in terms of running time, we observe it finds a model that has lower generalization (test) error than SGD, and this difference is even more significant in large batch training. 
We conduct several interesting experiments to support our conjecture that the trust region method can avoid sharp local minimas.
Non-Parametric Neuro-Adaptive Control Subject to Task Specifications
Authors: Christos Verginis, Zhe Xu, ufuk topcu

We develop a learning-based algorithm for the control of autonomous systems governed by unknown, nonlinear dynamics to satisfy user-specified spatio-temporal tasks expressed as signal temporal logic specifications. Most existing algorithms either assume certain parametric forms for the unknown dynamic terms or resort to unnecessarily large control inputs in order to provide theoretical guarantees. 
The proposed algorithm addresses these drawbacks by integrating neural-network-based learning with adaptive control. More specifically, the algorithm learns a controller, represented as a neural network, using training data that correspond to a collection of system parameters and tasks. These parameters and tasks are derived by varying the nominal parameters and the spatio-temporal constraints of the user-specified task, respectively.  It then incorporates this neural network into an online closed-form adaptive control policy in such a way that the resulting behavior satisfies the user-defined task. The proposed algorithm does not use any a priori information on the unknown dynamic terms or any approximation schemes. We provide formal theoretical guarantees on the satisfaction of the task. Numerical experiments on a robotic manipulator and a unicycle robot demonstrate that the proposed algorithm guarantees the satisfaction of 50 user-defined tasks, and outperforms control policies that do not employ online adaptation or the neural-network controller. Finally, we show that the proposed algorithm achieves greater performance than standard reinforcement-learning algorithms in the pendulum benchmarking environment.
Classless Association using Neural Networks
Authors: Federico Raue, Sebastian Palacio, Andreas Dengel, Marcus Liwicki

The goal of this paper is to train a model based on the relation between two instances that represent the same unknown class.  This scenario is inspired by the Symbol Grounding Problem and the association learning in infants.  We propose a novel model called Classless Association.  It has two parallel Multilayer Perceptrons (MLP) that uses one network as a target of the other network, and vice versa.  In addition, the presented model is trained based on an EM-approach, in which the output vectors are matched against a statistical distribution.  We generate four classless datasets based on MNIST, where the input is two different instances of the same digit.  In addition,  the digits have a uniform distribution.  Furthermore, our classless association model is evaluated against two scenarios: totally supervised and totally unsupervised.  In the first scenario, our model reaches a good performance in terms of accuracy and the classless constraint.  In the second scenario, our model reaches better results against two clustering algorithms.
Synthetic Reduced Nearest Neighbor Model for Regression
Authors: Pooya Tavallali, Vahid Behzadan, Mukesh Singhal

Nearest neighbor models are among the most established and accurate approaches to machine learning. In this paper, we investigate Synthetic Reduced Nearest Neighbor (SRNN) as a novel approach to regression tasks. Existing prototype nearest neighbor models are initialized by training a k-means model over each class. However, such initialization is only applicable to classification tasks. In this work, we propose a novel initialization and expectation maximization approach for enabling the application of SRNN to regression. The proposed initialization approach is based on applying the k-means algorithm on the target responses of samples to create various clusters of targets. This is proceeded by learning several centroids in the input space for each cluster found over the targets. Essentially, the initialization consists of finding target clusters and running k-means in the space of feature vectors for the corresponding target cluster. The optimization procedure consists of applying an expectation maximization approach similar to the k-means algorithm that optimizes the centroids in the input space. This algorithm is comprised of two steps: (1) The assignment step, where assignments of the samples to each centroid is found and the target response (i.e., prediction) of each centroid is determined; and (2) the update/centroid step, where each centroid is updated such that the loss function of the entire model is minimized. We will show that the centroid step operates over all samples via solving a weighted binary classification. However, the centroid step is NP-hard and no surrogate objective function exists for solving this problem. Therefore, a new surrogate is proposed to approximate the solution for the centroid step. Furthermore, we consider the consistency of the model, and show that the model is consistent under mild assumptions. The bias-variance relationship in this model is also discussed. We report the empirical evaluation of the proposed SRNN regression model in comparison to several state-of-the-art techniques.
Meta-RCNN: Meta Learning for Few-Shot Object Detection
Authors: Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi

Despite significant advances in object detection in recent years, training effective detectors in a small data regime remains an open challenge. Labelling training data for object detection is extremely expensive, and there is a need to develop techniques that can generalize well from small amounts of labelled data. We investigate this problem of few-shot object detection, where a detector has access to only limited amounts of annotated data. Based on the recently evolving meta-learning principle, we propose a novel meta-learning framework for object detection named ``Meta-RCNN", which learns the ability to perform few-shot detection via meta-learning. Specifically, Meta-RCNN learns an object detector in an episodic learning paradigm on the (meta) training data. This learning scheme helps acquire a prior which enables Meta-RCNN to do few-shot detection on novel tasks. Built on top of the Faster RCNN model, in Meta-RCNN, both the Region Proposal Network (RPN) and the object classification branch are meta-learned. The meta-trained RPN learns to provide class-specific proposals, while the object classifier learns to do few-shot classification. The novel loss objectives and learning strategy of Meta-RCNN can be trained in an end-to-end manner. We demonstrate the effectiveness of Meta-RCNN in addressing few-shot detection on Pascal VOC dataset and achieve promising results.
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression
Authors: Dailan He, Ziming Yang, Yan Wang, Yuan Chen, Qi Zhang, Hongwei Qin

It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications. One of the most critical issues preventing it from being practical is the non-deterministic calculation, which makes the probability prediction cross-platform inconsistent and frustrates successful decoding. We propose to solve this problem by introducing well-developed post-training quantization and making the model inference integer-arithmetic-only, which is much simpler than presently existing training and fine-tuning based approaches yet still keeps the superior rate-distortion performance of learned image compression. Based on that, we further improve the discretization of the entropy parameters and extend the deterministic inference to fit Gaussian mixture models. With our proposed methods, the current state-of-the-art image compression models can infer in a cross-platform consistent manner, which makes the further development and practice of learned image compression more promising.
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems
Authors: Atsushi Nitanda, Geoffrey Chinot, Taiji Suzuki

Recently, several studies have proven the global convergence and generalization abilities of the gradient descent method for two-layer ReLU networks. Most studies especially focused on the regression problems with the squared loss function, except for a few, and the importance of the positivity of the neural tangent kernel has been pointed out. However, the performance of gradient descent on classification problems using the logistic loss function has not been well studied, and further investigation of this problem structure is possible. In this work, we demonstrate that the separability assumption using a neural tangent model is more reasonable than the positivity condition of the neural tangent kernel and provide a refined convergence analysis of the gradient descent for two-layer networks with smooth activations. A remarkable point of our result is that our convergence and generalization bounds have much better dependence on the network width in comparison to related studies. Consequently, our theory significantly enlarges a class of over-parameterized networks with provable generalization ability, with respect to the network width, while most studies require much higher over-parameterization.