nils-herrmann/clean_scirepeval huggingface dataset:
The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples
Authors: Luke Hewitt, Andrea Gane, Tommi Jaakkola, Joshua B. Tenenbaum

Hierarchical Bayesian methods have the potential to unify many related tasks (e.g. k-shot classification, conditional, and unconditional generation) by framing each as inference within a single generative model. We show that existing approaches for learning such models can fail on expressive generative networks such as PixelCNNs, by describing the global distribution with little reliance on latent variables. To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class; the result, which we call a Variational Homoencoder (VHE), may be understood as training a hierarchical latent variable model which better utilises latent variables in these cases. Using this framework enables us to train a hierarchical PixelCNN for the Omniglot dataset, outperforming all existing models on test set likelihood. With a single model we achieve both strong one-shot generation and near human-level classification, competitive with state-of-the-art discriminative classifiers. The VHE objective extends naturally to richer dataset structures such as factorial or hierarchical categories, as we illustrate by training models to separate character content from simple variations in drawing style, and to generalise the style of an alphabet to new characters.
Local Image-to-Image Translation via Pixel-wise Highway Adaptive Instance Normalization
Authors: Wonwoong Cho, Seunghwan Choi, Junwoo Park, David Keetae Park, Tao Qin, Jaegul Choo

Recently, image-to-image translation has seen a significant success. Among many approaches, image translation based on an exemplar image, which contains the target style information, has been popular, owing to its capability to handle multimodality as well as its suitability for practical use. However, most of the existing methods extract the style information from an entire exemplar and apply it to the entire input image, which introduces excessive image translation in irrelevant image regions. In response, this paper proposes a novel approach that jointly extracts out the local masks of the input image and the exemplar as targeted regions to be involved for image translation. In particular, the main novelty of our model lies in (1) co-segmentation networks for local mask generation and (2) the local mask-based highway adaptive instance normalization technique. We demonstrate the quantitative and the qualitative evaluation results to show the advantages of our proposed approach. Finally, the code is available at https://github.com/AnonymousIclrAuthor/Highway-Adaptive-Instance-Normalization
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning
Authors: Amrita Saha, Shafiq Joty, Steven Hoi

Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic form of numerical reasoning over text  in Machine Reading Comprehension (MRC). However, to achieve this, contemporary NMNs need strong supervision in executing the query as a specialized program over the reasoning modules and fail to generalize to more open-ended settings where such supervision is not readily available. In this work, we propose Weakly Supervised Neuro-Symbolic Module Network (WNSMN) trained with answers as the sole supervision for numerical reasoning based MRC. It learns to execute a noisy heuristic program obtained from dependency parsing of the query as discrete actions over both neural and symbolic reasoning modules and trains it end-to-end in a reinforcement learning framework with discrete reward from answer matching. On the numerical-answer subset of the DROP dataset, WNSMN outperforms NMN by 32% and the reasoning-free language model GenBERT by 8% in exact match accuracy when trained under comparable weak supervised settings. This showcases the effectiveness and generalizability of modular networks that can handle explicit discrete reasoning over noisy programs in an end-to-end manner.
Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings
Authors: Shweta Mahajan, Iryna Gurevych, Stefan Roth

Learned joint representations of images and text form the backbone of several important cross-domain tasks such as image captioning. Prior work mostly maps both domains into a common latent representation in a purely supervised fashion. This is rather restrictive, however, as the two domains follow distinct generative processes. Therefore, we propose a novel semi-supervised framework, which models shared information between domains and domain-specific information separately. 
The information shared between the domains is aligned with an invertible neural network. Our model integrates normalizing flow-based priors for the domain-specific information, which allows us to learn diverse many-to-many mappings between the two domains. We demonstrate the effectiveness of our model on diverse tasks, including image captioning and text-to-image synthesis.
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
Authors: Minhao LIU, Ailing Zeng, Qiuxia LAI, Ruiyuan Gao, Min Li, Jing Qin, Qiang Xu

Time series signal analysis plays an essential role in many applications, e.g., activity recognition and healthcare monitoring.
Recently, features extracted with deep neural networks (DNNs) have shown to be more effective than conventional hand-crafted ones.
However, most existing solutions rely solely on the network to extract information carried in the raw signal, regardless of its inherent physical and statistical properties, leading to sub-optimal performance particularly under a limited amount of training data.
In this work, we propose a novel tree-structured wavelet neural network for time series signal analysis, namely \emph{T-WaveNet}, taking advantage of an inherent property of various types of signals, known as the \emph{dominant frequency range}. Specifically, with \emph{T-WaveNet}, we first conduct frequency spectrum energy analysis of the signals to get a set of dominant frequency subbands. Then, we construct a tree-structured network that iteratively decomposes the input signal into various frequency subbands with similar energies. Each node on the tree is built with an invertible neural network (INN) based wavelet transform unit. Such a disentangled representation learning method facilitates a more effective extraction of the discriminative features, as demonstrated with the comprehensive experiments on various real-life time series classification datasets.
Learning with Social Influence through  Interior Policy Differentiation
Authors: Hao Sun, Bo Dai, Jiankai Sun, Zhenghao Peng, Guodong Xu, Dahua Lin, Bolei Zhou

Animals develop novel skills not only through the interaction with the environment but also from the influence of the others. In this work we model the social influence into the scheme of reinforcement learning, enabling the agents to learn both from the environment and from their peers. Specifically, we first define a metric to measure the distance between policies then quantitatively derive the definition of uniqueness. Unlike previous precarious joint optimization approaches, the social uniqueness motivation in our work is imposed as a constraint to encourage the agent to learn a policy different from the existing agents while still solve the primal task. The resulting algorithm, namely Interior Policy Differentiation (IPD), brings about performance improvement as well as a collection of policies that solve a given task with distinct behaviors
Gumbel-Matrix Routing for Flexible Multi-task Learning
Authors: Krzysztof Maziarz, Efi Kokiopoulou, Andrea Gesmundo, Luciano Sbaiz, Gabor Bartok, Jesse Berent

This paper proposes a novel per-task routing method for multi-task applications. Multi-task neural networks can learn to transfer knowledge across different tasks by using parameter sharing. However, sharing parameters between unrelated tasks can hurt performance. To address this issue, routing networks can be applied to learn to share each group of parameters with a different subset of tasks to better leverage tasks relatedness. However, this use of routing methods requires to address the challenge of learning the routing jointly with the parameters of a modular multi-task neural network. We propose the Gumbel-Matrix routing, a novel multi-task routing method based on the Gumbel-Softmax, that is designed to learn fine-grained parameter sharing. When applied to the Omniglot benchmark, the proposed method improves the state-of-the-art error rate by 17%.
Non-Autoregressive Neural Machine Translation
Authors: Jiatao Gu, James Bradbury, Caiming Xiong, Victor O.K. Li, Richard Socher

Existing approaches to neural machine translation condition each output word on previously generated outputs. We introduce a model that avoids this autoregressive property and produces its outputs in parallel, allowing an order of magnitude lower latency during inference. Through knowledge distillation, the use of input token fertilities as a latent variable, and policy gradient fine-tuning, we achieve this at a cost of as little as 2.0 BLEU points relative to the autoregressive Transformer network used as a teacher. We demonstrate substantial cumulative improvements associated with each of the three aspects of our training strategy, and validate our approach on IWSLT 2016 English–German and two WMT language pairs. By sampling fertilities in parallel at inference time, our non-autoregressive model achieves near-state-of-the-art performance of 29.8 BLEU on WMT 2016 English–Romanian.
Improving Confident-Classifiers For Out-of-distribution Detection
Authors: Sachin Vernekar, Ashish Gaurav, Vahdat Abdelzad, Taylor Denouden, Rick Salay, Krzysztof Czarnecki

Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution).  Therefore, detecting out-of-distribution  (OOD)  samples is very important to avoid classification errors. In the context of OOD detection for image classification,  one of the recent approaches proposes training a classifier called “confident-classifier” by minimizing the standard cross-entropy loss on in-distribution samples and minimizing the KLdivergence between the predictive distribution of OOD samples in the low-density“boundary” of in-distribution and the uniform distribution (maximizing the entropy of the outputs).  Thus, the samples could be detected as OOD if they have low confidence or high entropy.  In this paper, we analyze this setting both theoretically and experimentally.  We also propose a novel algorithm to generate the“boundary” OOD samples to train a classifier with an explicit “reject” class for OOD samples.  We compare our approach against several recent classifier-based OOD detectors including the confident-classifiers on MNIST and Fashion-MNISTdatasets.  Overall the proposed approach consistently performs better than others across most of the experiments.
Generative Imputation and Stochastic Prediction
Authors: Mohammad Kachuee, Kimmo Kärkkäinen, Orpaz Goldstein, Sajad Darabi, Majid Sarrafzadeh

In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing values. However, the existence of missing values is synonymous with uncertainties not only over the distribution of missing values but also over target class assignments that require careful consideration. In this paper, we propose a simple and effective method for imputing missing features and estimating the distribution of target assignments given incomplete data. In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish. Following this, a predictor network is trained using the imputed samples from the generator network to capture the classification uncertainties and make predictions accordingly. The proposed method is evaluated on CIFAR-10 image dataset as well as three real-world tabular classification datasets, under different missingness rates and structures. Our experimental results show the effectiveness of the proposed method in generating imputations as well as providing estimates for the class uncertainties in a classification task when faced with missing values.
Dependent Bidirectional RNN with Extended-long Short-term Memory
Authors: Yuanhang Su, Yuzhong Huang, C.-C. Jay Kuo

In this work, we first conduct mathematical analysis on the memory, which is
defined as a function that maps an element in a sequence to the current output,
of three RNN cells; namely, the simple recurrent neural network (SRN), the long
short-term memory (LSTM) and the gated recurrent unit (GRU). Based on the
analysis, we propose a new design, called the extended-long short-term memory
(ELSTM), to extend the memory length of a cell. Next, we present a multi-task
RNN model that is robust to previous erroneous predictions, called the dependent
bidirectional recurrent neural network (DBRNN), for the sequence-in-sequenceout
(SISO) problem. Finally, the performance of the DBRNN model with the
ELSTM cell is demonstrated by experimental results.
Does Entity Abstraction Help Generative Transformers Reason?
Authors: Nicolas Gontier, Siva Reddy, Christopher Pal

Pre-trained language models (LMs) often struggle to reason logically or generalize in a compositional fashion. Recent work suggests that incorporating external entity knowledge can improve language models' abilities to reason and generalize. However the effect of explicitly providing entity abstraction remains unclear, especially with recent studies suggesting that pre-trained models already encode some of that knowledge in their parameters. In this work, we study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on three different NLP tasks requiring different forms of logical reasoning: (1) compositional language understanding with text-based relational reasoning (CLUTRR), (2) multi-hop question answering (HotpotQA), and (3) conversational question answering (CoQA). We propose and empirically explore three different ways to add such abstraction: (i) as additional input embeddings, (ii) as a separate sequence to encode, and (iii) as an auxiliary prediction task for the model. Overall our analysis demonstrate that models with abstract entity knowledge performs slightly better than without it. However, our experiments also show that the benefits strongly depend on the technique used and the task at hand. The best abstraction aware model achieved an overall accuracy of 88.8% compared to the baseline model achieving 62.3% on CLUTRR. In addition, abstraction-aware models showed improved compositional generalization in both interpolation and extrapolation settings. However, for HotpotQA and CoQA, we find that F1 scores improve by only 0.5% on average. Our results suggest that the benefits of explicit abstraction could be very significant in formally defined logical reasoning settings such as CLUTRR, but point to the notion that explicit abstraction is likely less beneficial for NLP tasks having less formal logical structure.
Neural Thompson Sampling
Authors: Weitong ZHANG, Dongruo Zhou, Lihong Li, Quanquan Gu

Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-armed bandit problems. In this paper, we propose a new algorithm, called Neural Thompson Sampling, which adapts deep neural networks for both exploration and exploitation. At the core of our algorithm is a novel posterior distribution of the reward, where its mean is the neural network approximator, and its variance is built upon the neural tangent features of the corresponding neural network. We prove that, provided the underlying reward function is bounded, the proposed algorithm is guaranteed to achieve a cumulative regret of $O(T^{1/2})$, which matches the regret of other contextual bandit algorithms in terms of total round number $T$. Experimental comparisons with other benchmark bandit algorithms on various data sets corroborate our theory.
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
Authors: Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Mysore Nagaraj, Praneeth Netrapalli

Q-learning is a popular Reinforcement Learning (RL) algorithm which is widely used in practice with function approximation (Mnih et al., 2015). In contrast, existing theoretical results are pessimistic about Q-learning. For example, (Baird, 1995) shows that Q-learning does not converge even with linear function approximation for linear MDPs. Furthermore, even for tabular MDPs with synchronous updates, Q-learning was shown to have sub-optimal sample complexity (Li et al., 2021, Azar et al., 2013). The goal of this work is to bridge the gap between practical success of Q-learning and the relatively pessimistic theoretical results. The starting point of our work is the observation that in practice, Q-learning is used with two important modifications: (i) training with two networks, called online network and target network simultaneously (online target learning, or OTL) , and (ii) experience replay (ER) (Mnih et al., 2015). While they have been observed to play a significant role in the practical success of Q-learning, a thorough theoretical understanding of how these two modifications improve the convergence behavior of Q-learning has been missing in literature. By carefully combining the Q-learning with OTL and reverse experience replay (RER) (a form of experience replay), we present novel methods Q-Rex and Q-RexDaRe (Q-Rex+data reuse). We show that Q-Rex efficiently finds the optimal policy for linear MDPs and provide non-asymptotic bounds on sample complexity -- the first such result for a Q-learning method with linear MDPs. Furthermore, we demonstrate that Q-RexDaRe in fact achieves near optimal sample complexity in the tabular setting, improving upon the existing results for vanilla Q-learning.
Measuring and mitigating interference in reinforcement learning
Authors: Vincent Liu, Adam M White, Hengshuai Yao, Martha White

Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. But, before we overcome interference we must understand it better. In this work, we first provide a definition and novel measure of interference for value-based control methods such as Fitted Q Iteration and DQN. We systematically evaluate our measure of interference, showing that it correlates with forgetting, across a variety of network architectures. Our new interference measure allows us to ask novel scientific questions about commonly used deep learning architectures and develop new learning algorithms. In particular we show that updates on the last layer result in significantly higher interference than updates internal to the network. Lastly, we introduce a novel online-aware representation learning algorithm to minimize interference, and we empirically demonstrate that it improves stability and has lower interference.
Neural Tree Transducers for Tree to Tree Learning
Authors: João Sedoc, Dean Foster, Lyle Ungar

We introduce a novel approach to tree-to-tree learning, the neural tree transducer (NTT), a top-down depth first context-sensitive tree decoder, which is paired with recursive neural encoders. Our method works purely on tree-to-tree manipulations rather than sequence-to-tree or tree-to-sequence and is able to encode and decode multiple depth trees. We compare our method to sequence-to-sequence models applied to serializations of the trees and show that our method outperforms previous methods for tree-to-tree transduction.
Learning to Control PDEs with Differentiable Physics
Authors: Philipp Holl, Nils Thuerey, Vladlen Koltun

Predicting outcomes and planning interactions with the physical world are long-standing goals for machine learning. A variety of such tasks involves continuous physical systems, which can be described by partial differential equations (PDEs) with many degrees of freedom. Existing methods that aim to control the dynamics of such systems are typically limited to relatively short time frames or a small number of interaction parameters. We present a novel hierarchical predictor-corrector scheme which enables neural networks to learn to understand and control complex nonlinear physical systems over long time frames. We propose to split the problem into two distinct tasks: planning and control. To this end, we introduce a predictor network that plans optimal trajectories and a control network that infers the corresponding control parameters. Both stages are trained end-to-end using a differentiable PDE solver. We demonstrate that our method successfully develops an understanding of complex physical systems and learns to control them for tasks involving PDEs such as the incompressible Navier-Stokes equations.
Gesture2Vec: Clustering Gestures using  Representation Learning Methods for Co-speech Gesture Generation
Authors: Payam Jome Yazdian, Mo Chen, Angelica Lim

Co-speech gestures are a principal component in conveying messages and enhancing interaction experiences between humans. Similarly, the co-speech gesture is a key ingredient in human-agent interaction including both virtual agents and robots. Existing machine learning approaches have yielded only marginal success in learning speech-to-motion at the frame level. Current methods generate repetitive gesture sequences that lack appropriateness with respect to the speech context. In this paper, we propose a Gesture2Vec model using representation learning methods to learn the relationship between semantic features and corresponding gestures. We propose a vector-quantized variational autoencoder structure as well as training techniques to learn a rigorous representation of gesture sequences. Furthermore, we use a machine translation model that takes input text and translates it into a discrete sequence of associated gesture chunks in the learned gesture space. Ultimately, we use translated quantized gestures from the input text as an input to the autoencoder’s decoder to produce gesture sequences. The resulting gestures can be applied to both virtual agents and humanoid robots. Subjective and objective evaluations confirm the success of our approach in terms of appropriateness, human-likeness, and diversity.