nils-herrmann/clean_scirepeval huggingface dataset:
Policy Message Passing: A New Algorithm for Probabilistic Graph Inference
Authors: Zhiwei Deng, Greg Mori

A general graph-structured neural network architecture operates on graphs through two core components: (1) complex enough message functions; (2) a fixed information aggregation process. In this paper, we present the Policy Message Passing algorithm, which takes a probabilistic perspective and reformulates the whole information aggregation as stochastic sequential processes. The algorithm works on a much larger search space, utilizes reasoning history to perform inference, and is robust to noisy edges. We apply our algorithm to multiple complex graph reasoning and prediction tasks and show that our algorithm consistently outperforms state-of-the-art graph-structured models by a significant margin.
Learning-to-Count by Learning-to-Rank: Weakly Supervised Object Counting & Localization Using Only Pairwise Image Rankings
Authors: Adriano C. D'Alessandro, Ali Mahdavi Amiri, Ghassan Hamarneh

Object counting and localization in dense scenes is a challenging class of image analysis problems that typically requires labour intensive annotations to learn to solve. We propose a form of weak supervision that only requires object-based pairwise image rankings. These annotations can be collected rapidly with a single click per image pair and supply a weak signal for object quantity. However, the problem of actually extracting object counts and locations from rankings is challenging. Thus, we introduce adversarial density map generation, a strategy for regularizing the features of a ranking network such that the features correspond to an object proposal map where each proposal must be a Gaussian blob that integrates to 1. This places a soft integer and soft localization constraint on the representation, which encourages the network to satisfy the provided ranking constraints by detecting objects. We then demonstrate the effectiveness of our method for exploiting pairwise image rankings as a weakly supervised signal for object counting and localization on several datasets, and show results with a performance that approaches that of fully supervised methods on many counting benchmark datasets while relying on data that can be collected with a fraction of the annotation burden.
MVP: Multivariate polynomials for conditional generation
Authors: Grigorios Chrysos, Yannis Panagakis

Conditional Generative Adversarial Nets (cGANs) have been widely adopted for image generation. cGANs take  i) a noise vector and ii) a conditional variable as input. The conditional variable can be discrete (e.g., a class label) or continuous (e.g., an input image) resulting into class-conditional (image) generation and image-to-image translation models, respectively. However, depending on whether the conditional variable is discrete or continuous, various cGANs employ substantially different deep architectures and loss functions for their training. In this paper, we propose a novel framework, called MVP, for conditional data generation. MVP resorts to multivariate polynomials of higher-order and treats in a unified way both discrete and continuous conditional variables. MVP is highly expressive, capturing higher-order auto- and cross-correlations of input variables (noise vector and conditional variable). Tailored sharing schemes are designed between the polynomial’s parameter tensors, which result in simple recursive formulas. MVP can synthesize realistic images in both class-conditional and image-to-image translation tasks even in the absence of activation functions between the layers.
Translatotron 2: Robust direct speech-to-speech translation
Authors: Ye Jia, Michelle Tadmor Ramanovich, Tal Remez, Roi Pomerantz

We present Translatotron 2, a neural direct speech-to-speech translation model that can be trained end-to-end. Translatotron 2 consists of a speech encoder, a phoneme decoder, a mel-spectrogram synthesizer, and an attention module that connects all the previous three components. Experimental results suggest that Translatotron 2 outperforms the original Translatotron by a large margin in terms of translation quality and predicted speech naturalness, and drastically improves the robustness of the predicted speech by mitigating over-generation, such as babbling or long pause. We also propose a new method for retaining the source speaker's voice in the translated speech. The trained model is restricted to retain the source speaker's voice, but unlike the original Translatotron, it is not able to generate speech in a different speaker's voice, making the model more robust for production deployment, by mitigating potential misuse for creating spoofing audio artifacts. When the new method is used together with a simple concatenation-based data augmentation, the trained Translatotron 2 model is able to retain each speaker's voice for input with speaker turns.
A Framework for the Quantitative Evaluation of Disentangled Representations
Authors: Cian Eastwood, Christopher K. I. Williams

Recent AI research has emphasised the importance of learning disentangled representations of the explanatory factors  behind data. Despite the growing interest in models which can learn such representations, visual inspection remains the standard evaluation metric. While various desiderata have been implied in recent definitions, it is currently unclear what exactly makes one disentangled representation better than another. In this work we propose a framework for the quantitative evaluation of disentangled representations when the ground-truth latent structure is available. Three criteria are explicitly defined and quantified to elucidate the quality of learnt representations and thus compare models on an equal basis. To illustrate the appropriateness of the framework, we employ it to compare quantitatively the representations learned by recent state-of-the-art models.
Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach
Authors: Shubham Gupta, Ambedkar Dukkipati

Formulating the reinforcement learning (RL) problem in the framework of probabilistic inference not only offers a new perspective about RL, but also yields practical algorithms that are more robust and easier to train. While this connection between RL and probabilistic inference has been extensively studied in the single-agent setting, it has not yet been fully understood in the multi-agent setup. In this paper, we pose the problem of multi-agent reinforcement learning as the problem of performing inference in a particular graphical model. We model the environment, as seen by each of the agents, using separate but related Markov decision processes. We derive a practical off-policy maximum-entropy actor-critic algorithm that we call Multi-agent Soft Actor-Critic (MA-SAC) for performing approximate inference in the proposed model using variational inference. MA-SAC can be employed in both cooperative and competitive settings. Through experiments, we demonstrate that MA-SAC outperforms a strong baseline on several multi-agent scenarios. While MA-SAC is one resultant multi-agent RL algorithm that can be derived from the proposed probabilistic framework, our work provides a unified view of maximum-entropy algorithms in the multi-agent setting.
Simple Black-box Adversarial Attacks
Authors: Chuan Guo, Jacob R. Gardner, Yurong You, Andrew G. Wilson, Kilian Q. Weinberger

The construction of adversarial images is a search problem in high dimensions within a small region around a target image. The goal is to find an imperceptibly modified image that is misclassified by a target model. In the black-box setting, only sporadic feedback is provided through occasional model evaluations. In this paper we provide a new algorithm whose search strategy is based on an intriguingly simple iterative principle: We randomly pick a low frequency component of the discrete cosine transform (DCT) and either add or subtract it to the target image. Model evaluations are only required to identify whether an operation decreases the adversarial loss. Despite its simplicity, the proposed method can be used for targeted and untargeted attacks --- resulting in previously unprecedented query efficiency in both settings. We require a median of 600 black-box model queries (ResNet-50) to produce an adversarial ImageNet image, and we successfully attack Google Cloud Vision with 2500 median queries, averaging to a cost of only $3 per image. We argue that our proposed algorithm should serve as a strong baseline for future adversarial black-box attacks, in particular because it is extremely fast and can be implemented in less than 20 lines of PyTorch code.
Deep Representations for Time-varying Brain Datasets
Authors: Sikun Lin, Shuyun Tang, Ambuj Singh

Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (functional magnetic resonance imaging) cannot capture the whole picture of underlying brain activities, and previous works lack the ability to learn and interpret the latent dynamics in brain architectures. In this paper, we build an efficient graph neural network model that incorporates both region-mapped fMRI sequences and structural connectivities obtained from DWI (diffusion-weighted imaging) as inputs. Through novel sample-level adaptive adjacency matrix learning and multi-resolution inner cluster smoothing, we find good representations of the latent brain dynamics. We also attribute inputs with integrated gradients, which enables us to infer (1) highly involved brain connections and subnetworks for each task (2) keyframes of imaging sequences along the temporal axis, and (3) subnetworks that discriminate between individual subjects. This ability to identify critical subnetworks that characterize brain states across heterogeneous tasks and individuals is of great importance to neuroscience research. Extensive experiments and ablation studies demonstrate our proposed method's superiority and efficiency in spatial-temporal graph signal modeling with insightful interpretations of brain dynamics.
Escaping Saddle Points in Nonconvex Minimax Optimization via Cubic-Regularized Gradient Descent-Ascent
Authors: Ziyi Chen, Qunwei Li, Yi Zhou

The gradient descent-ascent (GDA) algorithm has been widely applied to solve nonconvex minimax optimization problems. However, the existing GDA-type algorithms can only find first-order stationary points of the envelope function of nonconvex minimax optimization problems, which does not rule out the possibility to get stuck at suboptimal saddle points. In this paper, we develop Cubic-GDA -- the first GDA-type algorithm for escaping strict saddle points in nonconvex-strongly-concave minimax optimization. Specifically, the algorithm uses gradient ascent to estimate the second-order information of the minimax objective function, and it leverages the cubic regularization technique to efficiently escape the strict saddle points. Under standard smoothness assumptions on the objective function, we show that Cubic-GDA admits an intrinsic potential function whose value monotonically decreases in the minimax optimization process. Such a property leads to a desired global convergence of Cubic-GDA to a second-order stationary point at a sublinear rate. Moreover, we analyze the convergence rate of Cubic-GDA in the full spectrum of a gradient dominant-type nonconvex geometry. Our result shows that Cubic-GDA achieves an orderwise faster convergence rate than the standard GDA for a wide spectrum of gradient dominant geometry. Our study bridges minimax optimization with second-order optimization and may inspire new developments along this direction.
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
Authors: Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Yi-Chen Lo, Chia-Che Chang, Yu-Lun Liu, Yu-Lin Chang, Chia-Ping Chen, Chun-Yi Lee

Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier. However, our analysis indicates that the training objectives for the classifier in these methods may lead to a serious score mismatch issue, which corresponds to the situation that the estimated scores deviate from the true ones. Such an issue causes the samples to be misled by the deviated scores during the diffusion process, resulting in a degraded sampling quality. To resolve it, we theoretically formulate a novel training objective, called Denoising Likelihood Score Matching (DLSM) loss, for the classifier to match the gradients of the true log likelihood density. Our experimental evidences show that the proposed method outperforms the previous methods on both Cifar-10 and Cifar-100 benchmarks noticeably in terms of several key evaluation metrics. We thus conclude that, by adopting DLSM, the conditional scores can be accurately modeled, and the effect of the score mismatch issue is alleviated.
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
Authors: Wenyong Huang, Zhenhe Zhang, Yu Ting Yeung, Xin Jiang, Qun Liu

We introduce a new approach for speech pre-training named SPIRAL which works by learning denoising representation of perturbed data in a teacher-student framework. 
Specifically, given a speech utterance, we first feed the utterance to a teacher network to obtain corresponding representation. Then the same utterance is perturbed and fed to a student network. The student network is trained to output representation resembling that of the teacher. At the same time, the teacher network is updated as moving average of student's weights over training steps. In order to prevent representation collapse, we apply an in-utterance contrastive loss as pre-training objective and impose position randomization on the input to the teacher. SPIRAL achieves competitive or better results compared to state-of-the-art speech pre-training method wav2vec 2.0, with significant reduction of training cost (80% for BASE model, 65% for LARGE model). 
Furthermore, we address the problem of noise-robustness that is critical to real-world speech applications. We propose multi-condition pre-training by perturbing the student's input with various types of additive noise. We demonstrate that multi-condition pre-trained SPIRAL models are more robust to noisy speech (9.0% - 13.3% relative word error rate reduction on real noisy test data), compared to applying multi-condition training solely in the fine-tuning stage. Source code is available at https://github.com/huawei-noah/Speech-Backbones/tree/main/SPIRAL.
Dialogue Learning With Human-in-the-Loop
Authors: Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston

An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes.  Most research has focused on learning from fixed training sets of labeled data rather than interacting with a dialogue partner in an online fashion. In this paper we explore this direction in a reinforcement learning setting where the bot improves its question-answering ability from feedback a teacher gives following its generated responses. We build a simulator that tests various aspects of such learning in a synthetic environment, and introduce models that work in this regime.  Finally, real experiments with Mechanical Turk validate the approach.
Reinforced Pipeline Optimization: Behaving Optimally with Non-Differentiabilities
Authors: Aijun Bai, Dongdong Chen, Gang Hua, Lu Yuan

Many machine learning systems are implemented as pipelines. A pipeline is essentially a chain/network of information processing units. As information flows in and out and gradients vice versa, ideally, a pipeline can be trained end-to-end via backpropagation provided with the right supervision and loss function. However, this is usually impossible in practice, because either the loss function itself may be non-differentiable, or there may exist some non-differentiable units. One popular way to superficially resolve this issue is to separate a pipeline into a set of differentiable sub-pipelines and train them with isolated loss functions. Yet, from a decision-theoretical point of view, this is equivalent to making myopic decisions using ad hoc heuristics along the pipeline while ignoring the real utility, which prevents the pipeline from behaving optimally. In this paper, we show that by converting a pipeline into a stochastic counterpart, it can then be trained end-to-end in the presence of non-differentiable parts. Thus, the resulting pipeline is optimal under certain conditions with respect to any criterion attached to it. In experiments, we apply the proposed approach - reinforced pipeline optimization - to Faster R-CNN, a state-of-the-art object detection pipeline, and obtain empirically near-optimal object detectors consistent with its base design in terms of mean average precision.
Reference-Aware Language Models
Authors: Zichao Yang, Phil Blunsom, Chris Dyer, Wang Ling

We propose a general class of language models that treat reference as an explicit stochastic latent variable. This architecture allows models to create mentions of entities and their attributes by accessing external databases (required by, e.g., dialogue generation and recipe generation) and internal state (required by, e.g. language models which are aware of coreference). This facilitates the incorporation of information that can be accessed in predictable locations in databases or dis- course context, even when the targets of the reference may be rare words. Experiments on three tasks show our model variants outperform models based on deterministic attention.
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
Authors: Zhaoyang Lyu, Zhifeng Kong, Xudong XU, Liang Pan, Dahua Lin

3D point clouds are an important data format that captures 3D information for real world objects.  Since 3D point clouds scanned in the real world are often incomplete, it is important to recover the complete point cloud for many downstreaming applications. Most existing point cloud completion methods use the Chamfer Distance (CD) loss for training. The CD loss estimates correspondences between two point clouds by searching nearest neighbors, which does not capture the overall point distribution on the generated shape, and therefore likely leads to non-uniform point cloud generation. To tackle this problem, we propose a novel Point Diffusion-Refinement (PDR) paradigm for point cloud completion. PDR consists of a Conditional Generation Network (CGNet) and a ReFinement Network (RFNet). The CGNet uses a conditional generative model called the denoising diffusion probabilistic model (DDPM) to generate a coarse completion conditioned on the partial observation. DDPM establishes a one-to-one pointwise mapping between the generated point cloud and the uniform ground truth, and then optimizes the mean squared error loss to realize uniform generation. The RFNet refines the coarse output of the CGNet and further improves quality of the completed point cloud.  In terms of the architecture, we develop a novel dual-path architecture for both networks. The architecture can (1) effectively and efficiently extract multi-level features from partially observed point clouds to guide completion, and (2) accurately manipulate spatial locations of 3D points to obtain smooth surfaces and sharp details. Extensive experimental results on various benchmark datasets show that our PDR paradigm outperforms previous state-of-the-art methods for point cloud completion. In addition, with the help of the RFNet,  we can accelerate the iterative generation process of the DDPM by up to 50 times without much performance drop.
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
Authors: Shengyao Lu, Bang Liu, Keith G Mills, SHANGLING JUI, Di Niu

Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational reasoning framework based on reinforcement learning that reasons over relational graph data and explicitly mines underlying compositional logical rules from observations. R5 has strong systematicity and being robust to noisy data. It consists of a policy value network equipped with Monte Carlo Tree Search to perform recurrent relational prediction and a backtrack rewriting mechanism for rule mining. By alternately applying the two components, R5 progressively learns a set of explicit rules from data and performs explainable and generalizable relation prediction. We conduct extensive evaluations on multiple datasets. Experimental results show that R5 outperforms various embedding-based and rule induction baselines on relation prediction tasks while achieving a high recall rate in discovering ground truth rules.
Zero-shot Cross Language Text Classification
Authors: Dan Svenstrup, Jonas Meinertz Hansen, Ole Winther

Labeled text classification datasets are typically only available in a few select languages. In order to train a model for e.g news categorization in a language $L_t$ without a suitable text classification dataset there are two options. The first option is to create a new labeled dataset by hand, and the second option is to transfer label information from an existing labeled dataset in a source language $L_s$ to the target language $L_t$. In this paper we propose a method for sharing label information across languages by means of a language independent text encoder. The encoder will give almost identical representations to multilingual versions of the same text. This means that labeled data in one language can be used to train a classifier that works for the rest of the languages. The encoder is trained independently of any concrete classification task and can therefore subsequently be used for any classification task.  We show that it is possible to obtain good performance even in the case where only a comparable corpus of texts is available.
Revisiting Batch Normalization For Practical Domain Adaptation
Authors: Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, Xiaodi Hou

Deep neural networks (DNN) have shown unprecedented success in various computer vision applications such as image classification and object detection. However, it is still a common annoyance during the training phase, that one has to prepare at least thousands of labeled images to fine-tune a network to a specific domain. Recent study shows that a DNN has strong dependency towards the training dataset, and the learned features cannot be easily transferred to a different but relevant task without fine-tuning. In this paper, we propose a simple yet powerful remedy, called Adaptive Batch Normalization (AdaBN) to increase the generalization ability of a DNN. By modulating the statistics from the source domain to the target domain in all Batch Normalization layers across the network, our approach achieves deep adaptation effect for domain adaptation tasks. In contrary to other deep learning domain adaptation methods, our method does not require additional components, and is parameter-free. It archives state-of-the-art performance despite its surprising simplicity. Furthermore, we demonstrate that our method is complementary with other existing methods. Combining AdaBN with existing domain adaptation treatments may further improve model performance.