nils-herrmann/clean_scirepeval huggingface dataset:
Sparse Hierarchical Table Ensemble
Authors: Guy Farjon, Aharon Bar HIllel

Deep learning for tabular data is drawing increasing attention, with recent work attempting to boost the accuracy of neuron-based networks. However, when computational capacity is low as in Internet of Things (IoT), drone, or Natural User Interface (NUI) applications, such deep learning methods are deserted. We offer to enable deep learning capabilities using ferns (oblivious decision trees) instead of neurons, by constructing a Sparse Hierarchical Table Ensemble (S-HTE). S-HTE inference is dense at the beginning of the training process and becomes gradually sparse using an annealing mechanism, leading to an efficient final predictor. Unlike previous work with ferns, S-HTE learns useful internal representations, and it earns from increasing depth. Using a standard classification and regression benchmark, we show its accuracy is comparable to alternatives while having an order of magnitude lower computational complexity. Our PyTorch implementation is available at https://anonymous.4open.science/r/HTE_CTE-60EB/
Ask Question with Double Hints:  Visual Question Generation with Answer-awareness and Region-reference
Authors: Shen Kai, Lingfei Wu, Siliang Tang, Fangli Xu, Zhu Zhang, Yu Qiang, Yueting Zhuang

The task of visual question generation~(VQG) aims to generate human-like questions from an image and potentially other side information (e.g. answer type or the answer itself). Despite promising results have been achieved, previous works on VQG either i) suffer from one image to many questions mapping problem rendering the failure of generating referential and meaningful questions from an image, or ii) ignore rich correlations among the visual objects in an image and potential interactions between the side information and image. To address these limitations, we first propose a novel learning paradigm to generate visual questions with answer-awareness and region-reference. In particular, we aim to ask the right visual questions with \emph{Double Hints - textual answers and visual regions of interests}, effectively mitigating the existing one-to-many mapping issue. To this end, we develop a simple methodology to self-learn the visual hints without introducing any additional human annotations. Furthermore, to capture these sophisticated relationships, we propose a new double-hints guided Graph-to-Sequence learning framework that first models them as a dynamic graph and learns the implicit topology end-to-end, and then utilize a graph-to-sequence model to generate the questions with double hints. Our experiments on VQA2.0 and COCO-QA datasets demonstrate that our proposed model on this new setting can significantly outperform existing state-of-the-art baselines by a large margin.
Flow-based Recurrent Belief State Learning for POMDPs
Authors: Xiaoyu Chen, Yao Mu, Ping Luo, Shengbo Eben Li, Jianyu Chen

Partially Observable Markov Decision Process (POMDP) provides a principled and generic framework to model real world sequential decision making processes but yet remains unsolved, especially for high dimensional continuous space and unknown models. The main challenge lies in how to accurately obtain the belief state, which is the probability distribution over the unobservable environment states given historical information. Accurately calculating this belief state is a precondition for obtaining an optimal policy of POMDPs. Recent advances in deep learning techniques show great potential to learn good belief states, but they assume the belief states follow certain types of simple distributions such as diagonal Gaussian, which imposes strong restrictions to precisely capture the real belief states. In this paper, we introduce the \textbf{F}l\textbf{O}w-based \textbf{R}ecurrent \textbf{BE}lief \textbf{S}tate model (FORBES), which incorporates normalizing flows into the variational inference to learn general continuous belief states for POMDPs. Furthermore, we show that the learned belief states can be plugged into downstream RL algorithms to improve performance. In experiments, we show that our methods successfully capture the complex belief states that enable multi-modal predictions as well as high quality reconstructions, and results on challenging visual-motor control tasks show that our method achieves superior performance and sample efficiency.
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
Authors: Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, Daniel Soudry

Lately, post-training quantization methods have gained considerable attention, as they are simple to use, and require only a small unlabeled calibration set. This small dataset cannot be used to fine-tune the model without significant over-fitting. Instead, these methods only use the calibration set to set the activations' dynamic ranges. However, such methods always resulted in significant accuracy degradation, when used below 8-bits (except on small datasets). Here we aim to break the 8-bit barrier. To this end, we minimize the quantization errors of each layer separately by optimizing its parameters over the calibration set.  We empirically demonstrate that this approach is: (1) much less susceptible to over-fitting than the standard fine-tuning approaches, and can be used even on a very small calibration set; and (2) more powerful than previous methods, which only set the activations' dynamic ranges. Furthermore, we demonstrate how to optimally allocate the bit-widths for each layer, while constraining accuracy degradation or model compression by proposing a novel integer programming formulation. Finally, we suggest model global statistics tuning, to correct biases introduced during quantization. Together, these methods yield state-of-the-art results for both vision and text models. For instance, on ResNet50, we obtain less than 1\% accuracy degradation --- with 4-bit weights and activations in all layers, but the smallest two. Our code is available at,
Novelty Detection via Robust Variational Autoencoding
Authors: Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman

We propose a new method for novelty detection that can tolerate high corruption of the training points, whereas previous works assumed either no or very low corruption. Our method trains a robust variational autoencoder (VAE), which aims to generate a model for the uncorrupted training points. To gain robustness to high corruption, we incorporate the following four changes to the common VAE: 1. Extracting crucial features of the latent code by a carefully designed dimension reduction component for distributions; 2. Modeling the latent distribution as a mixture of Gaussian low-rank inliers and full-rank outliers, where the testing only uses the inlier model; 3. Applying the Wasserstein-1 metric for regularization, instead of the Kullback-Leibler (KL) divergence; and 4. Using a least absolute deviation error for reconstruction. We establish both robustness to outliers and suitability to low-rank modeling of the Wasserstein metric as opposed to the KL divergence. We illustrate state-of-the-art results on standard benchmarks for novelty detection.
Neural Architecture Search of SPD Manifold Networks
Authors: Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Erik Goron, Yan Wu, Luc Van Gool

In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks. Unlike the conventional NAS problem, our problem requires to search for a unique computational cell called the SPD cell. This SPD cell serves as a basic building block of SPD neural architectures. An efficient solution to our problem is important to minimize the extraneous manual effort in the SPD neural architecture design. To accomplish this goal, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design. Further, we model our new NAS problem using the supernet strategy, which models the architecture search problem as a one-shot training process of a single supernet. Based on the supernet modeling, we exploit a differentiable NAS algorithm on our relaxed continuous search space for SPD neural architecture search. Statistical evaluation of our method on drone, action, and emotion recognition tasks mostly provides better results than the state-of-the-art SPD networks and NAS algorithms. Empirical results show that our algorithm excels in discovering better SPD network design and providing models that are more than 3 times lighter than searched by state-of-the-art NAS algorithms.
Energy-Based Models for Continual Learning
Authors: Shuang Li, Yilun Du, Gido Martijn van de Ven, Antonio Torralba, Igor Mordatch

We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems. Instead of tackling continual learning via the use of external memory, growing models, or regularization, EBMs have a natural way to support a dynamically-growing number of tasks and classes and less interference with old tasks. We show that EBMs are adaptable to a more general continual learning setting where the data distribution changes without the notion of explicitly delineated tasks. We also find that EBMs outperform the baseline methods by a large margin on several continual learning benchmarks. These observations point towards EBMs as a class of models naturally inclined towards the continual learning regime.
Play to Grade: Grading Interactive Coding Games as Classifying Markov Decision Process
Authors: Allen Nie, Emma Brunskill, Chris Piech

Contemporary coding education often present students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games. While pedagogically compelling, grading such student programs requires dynamic user inputs, therefore they are difficult to grade by unit tests. In this paper we formalize the challenge of grading interactive programs as a task of classifying Markov Decision Processes (MDPs). Each student's program fully specifies an MDP where the agent needs to operate and decide, under reasonable generalization, if the dynamics and reward model of the input MDP conforms to a set of latent MDPs. We demonstrate that by experiencing a handful of latent MDPs millions of times, we can use the agent to sample trajectories from the input MDP and use a classifier to determine membership. Our method drastically reduces the amount of data needed to train an automatic grading system for interactive code assignments and present a challenge to state-of-the-art reinforcement learning generalization methods. Together with Code.org, we curated a dataset of 700k student submissions, one of the largest dataset of anonymized student submissions to a single assignment. This Code.org assignment had no previous solution for automatically providing correctness feedback to students and as such this contribution could lead to meaningful improvement in educational experience.
Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration
Authors: Jingwei Zhang, Niklas Wetzel, Nicolai Dorka, Joschka Boedecker, Wolfram Burgard

Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration. Commonly these signals are added as bonus rewards, which results in a mixture policy that neither conducts exploration nor task fulfillment resolutely.
In this paper, we instead learn separate intrinsic and extrinsic task policies and schedule between these different drives to accelerate exploration and stabilize learning. Moreover, we introduce a new type of intrinsic reward denoted as successor feature control (SFC), which is general and not task-specific. It takes into account statistics over complete trajectories and thus differs from previous methods that only use local information to evaluate intrinsic motivation. We evaluate our proposed scheduled intrinsic drive (SID) agent using three different environments with pure visual inputs: VizDoom, DeepMind Lab and DeepMind Control Suite. The results show a substantially improved exploration efficiency with SFC and the hierarchical usage of the intrinsic drives. A video of our experimental results can be found at https://gofile.io/?c=HpEwTd.
Scaleable input gradient regularization for adversarial robustness
Authors: Chris Finlay, Adam M Oberman

In this work we revisit gradient regularization for adversarial robustness with some new ingredients.  First, we derive new per-image theoretical robustness bounds based on local gradient information. These bounds strongly motivate input gradient regularization.  Second, we implement a scaleable version of input gradient regularization which avoids double backpropagation: adversarially robust ImageNet models are trained in 33 hours on four consumer grade GPUs.  Finally, we show experimentally and through theoretical certification that input gradient regularization is competitive with adversarial training. Moreover we demonstrate that gradient regularization does not lead to gradient obfuscation or gradient masking.
A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING
Authors: Chengchun Shi, Xiaoyu Wang, Shikai Luo, Rui Song, Hongtu Zhu, Jieping Ye

A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. The aim of this paper is to introduce a reinforcement learn- ing framework for carrying A/B testing in two-sided marketplace platforms, while characterizing the long-term treatment effects. Our proposed testing procedure allows for sequential monitoring and online updating. It is generally applicable to a variety of treatment designs in different industries. In addition, we systematically investigate the theoretical properties (e.g., size and power) of our testing procedure. Finally, we apply our framework to both synthetic data and a real-world data example obtained from a technological company to illustrate its advantage over the current practice.
Deep Positive Unlabeled Learning with a Sequential Bias
Authors: Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Kavin Chandrasekaran, Hamid Mansoor, Abdulaziz alajaji

For many domains, from video stream analytics to human activity recognition, only weakly-labeled datasets are available.
Worse yet, the given labels are often assigned sequentially, resulting in sequential bias. Current Positive Unlabeled (PU) classifiers, a state-of-the-art family of robust semi-supervised methods, are ineffective under sequential bias. In this work, we  propose DeepSPU, the first method to address this sequential bias problem. DeepSPU tackles the two interdependent subproblems of learning both the latent labeling process and the true class likelihoods within one architecture. We achieve this by developing a novel iterative learning strategy aided by theoretically-justified cost terms to avoid collapsing into a naive classifier. Our experimental studies demonstrate that DeepSPU outperforms state-of-the-art methods by over 10% on diverse real-world datasets.
Making Stochastic Neural Networks from Deterministic Ones
Authors: Kimin Lee, Jaehyung Kim, Song Chong, Jinwoo Shin

It has been believed that stochastic feedforward neural networks (SFNN) have several advantages beyond deterministic deep neural networks (DNN): they have more expressive power allowing multi-modal mappings and regularize better due to their stochastic nature. However, training SFNN is notoriously harder. In this paper, we aim at developing efficient training methods for large-scale SFNN, in particular using known architectures and pre-trained parameters of DNN. To this end, we propose a new intermediate stochastic model, called Simplified-SFNN, which can be built upon any baseline DNN and approximates certain SFNN by simplifying its upper latent units above stochastic ones. The main novelty of our approach is in establishing the connection between three models, i.e., DNN
-&gt; Simplified-SFNN -&gt; SFNN, which naturally leads to an efficient training procedure of the stochastic models utilizing pre-trained parameters of DNN. Using several popular DNNs, we show how they can be effectively transferred to the corresponding stochastic models for both multi-modal and classification tasks on MNIST, TFD, CIFAR-10, CIFAR-100 and SVHN datasets. In particular, our stochastic model built from the wide residual network has 28 layers and 36 million parameters, where the former consistently outperforms the latter for the classification tasks on CIFAR-10 and CIFAR-100 due to its stochastic regularizing effect.
Are all negatives created equal in contrastive instance discrimination?
Authors: Tiffany Cai, Jonathan Frankle, David J. Schwab, Ari S. Morcos

Self-supervised learning has recently begun to rival supervised learning on computer vision tasks. Many of the recent approaches have been based on contrastive instance discrimination (CID), in which the network is trained to recognize two augmented versions of the same instance (a query and positive while discriminating against a pool of other instances (negatives). Using MoCo v2 as our testbed, we divided negatives by their difficulty for a given query and studied which difficulty ranges were most important for learning useful representations. We found that a small minority of negatives--just the hardest 5%--were both necessary and sufficient for the downstream task to reach full accuracy. Conversely, the easiest 95% of negatives were unnecessary and insufficient. Moreover, we found that the very hardest 0.1% of negatives were not only unnecessary but also detrimental. Finally, we studied the properties of negatives that affect their hardness, and found that hard negatives were more semantically similar to the query, and that some negatives were more consistently easy or hard than we would expect by chance. Together, our results indicate that negatives play heterogeneous roles and CID may benefit from more intelligent negative treatment.
CLOPS: Continual Learning of Physiological Signals
Authors: Dani Kiyasseh, Tingting Zhu, David A. Clifton

Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i.i.d). This violation, however, is ubiquitous in clinical settings where data are streamed temporally and from a multitude of physiological sensors. To overcome this obstacle, we propose CLOPS, a replay-based continual learning strategy. In three continual learning scenarios based on three publically-available datasets, we show that CLOPS can outperform the state-of-the-art methods, GEM and MIR. Moreover, we propose end-to-end trainable parameters, which we term task-instance parameters, that can be used to quantify task difficulty and similarity. This quantification yields insights into both network interpretability and clinical applications, where task difficulty is poorly quantified.
Multi-turn Dialogue Response Generation in an Adversarial Learning Framework
Authors: Oluwatobi O. Olabiyi, Alan Salimov, Anish Khazane, Erik T. Mueller

We propose an adversarial learning approach to the generation of multi-turn dialogue responses. Our proposed framework, hredGAN, is based on conditional generative adversarial networks (GANs). The GAN's generator is a modified hierarchical recurrent encoder-decoder network (HRED) and the discriminator is a word-level bidirectional RNN that shares context and word embedding with the generator. During inference, noise samples conditioned on the dialogue history are used to perturb the generator's latent space to generate several possible responses. The final response is the one ranked best by the discriminator. The hredGAN shows major advantages over existing methods: (1) it generalizes better than networks trained using only the log-likelihood criterion, and (2) it generates longer, more informative and more diverse responses with high utterance and topic relevance even with limited training data. This superiority is demonstrated on the Movie triples and Ubuntu dialogue datasets with both the automatic and human evaluations.
Extending the Framework of Equilibrium Propagation to General Dynamics
Authors: Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio

The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists. Two major reasons are that neurons would need to send two different types of signal in the forward and backward phases, and that pairs of neurons would need to communicate through symmetric bidirectional connections.
We present a simple two-phase learning procedure for fixed point recurrent networks that addresses both these issues.
In our model, neurons perform leaky integration and synaptic weights are updated through a local mechanism.
Our learning method extends the framework of Equilibrium Propagation to general dynamics, relaxing the requirement of an energy function.
As a consequence of this generalization, the algorithm does not compute the true gradient of the objective function,
but rather approximates it at a precision which is proven to be directly related to the degree of symmetry of the feedforward and feedback weights.
We show experimentally that the intrinsic properties of the system lead to alignment of the feedforward and feedback weights, and that our algorithm optimizes the objective function.
Automatically Learning Feature Crossing from Model Interpretation for Tabular Data
Authors: Zhaocheng Liu, Qiang Liu, Haoli Zhang

Automatically feature generation is a major topic of automated machine learning. Among various feature generation approaches, feature crossing, which takes cross-product of sparse features, is a promising way to effectively capture the interactions among categorical features in tabular data. Previous works on feature crossing try to search in the set of all the possible cross feature fields. This is obviously not efficient when the size of original feature fields is large. Meanwhile, some deep learning-based methods combines deep neural networks and various interaction components. However, due to the existing of Deep Neural Networks (DNN), only a few cross features can be explicitly generated by the interaction components. Recently, piece-wise interpretation of DNN has been widely studied, and the piece-wise interpretations are usually inconsistent in different samples. Inspired by this, we give a definition of interpretation inconsistency in DNN, and propose a novel method called CrossGO, which selects useful cross features according to the interpretation inconsistency. The whole process of learning feature crossing can be done via simply training a DNN model and a logistic regression (LR) model. CrossGO can generate compact candidate set of cross feature fields, and promote the efficiency of searching. Extensive experiments have been conducted on several real-world datasets. Cross features generated by CrossGO can empower a simple LR model achieving approximate or even better performances comparing with complex DNN models.