GenAI4ELab/papercli-papers huggingface dataset:
Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning
Authors: Xiaobo Xia; Jiale Liu; Jun Yu; Xu Shen; Bo Han; Tongliang Liu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Deep learning methods nowadays rely on massive data, resulting in substantial costs of data storage and model training. Data selection is a useful tool to alleviate such costs, where a coreset of massive data is extracted to practically perform on par with full data. Based on carefully-designed score criteria, existing methods first count the score of each data point and then select the data points whose scores lie in a certain range to construct a coreset. These methods work well in their respective preconceived scenarios but are not robust to the change of scenarios, since the optimal range of scores varies as the scenario changes. The issue limits the application of these methods, because realistic scenarios often mismatch preconceived ones, and it is inconvenient or unfeasible to tune the criteria and methods accordingly. In this paper, to address the issue, a concept of the moderate coreset is discussed. Specifically, given any score criterion of data selection, different scenarios prefer data points with scores in different intervals. As the score median is a proxy of the score distribution in statistics, the data points with scores close to the score median can be seen as a proxy of full data and generalize different scenarios, which are used to construct the moderate coreset. As a proof-of-concept, a universal method that inherits the moderate coreset and uses the distance of a data point to its class center as the score criterion, is proposed to meet complex realistic scenarios. Extensive experiments confirm the advance of our method over prior state-of-the-art methods, leading to a strong baseline for future research. The implementation is available at https://github.com/tmllab/Moderate-DS.
Understanding The Robustness of Self-supervised Learning Through Topic Modeling
Authors: Zeping Luo; Shiyou Wu; Cindy Weng; Mo Zhou; Rong Ge
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Self-supervised learning has significantly improved the performance of many NLP tasks. However, how can self-supervised learning discover useful features, and why is it better than traditional approaches such as probabilistic models are still largely unknown. In this paper, we focus on the context of topic modeling and highlight a key advantage of self-supervised learning - when applied to data generated by topic models, self-supervised learning can be oblivious to the specific model, and hence is less susceptible to model misspecification. In particular, we prove that commonly used self-supervised objectives based on reconstruction or contrastive samples can both recover useful posterior information for general topic models. Empirically, we show that the same objectives can perform on par with posterior inference using the correct model, while outperforming posterior inference using misspecified models.
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting
Authors: Xiajun Jiang; Ryan Missel; Zhiyuan Li; Linwei Wang
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Modern applications increasingly require learning and forecasting latent dynamics from high-dimensional time-series. Compared to univariate time-series forecasting, this adds a new challenge of reasoning about the latent dynamics of an unobserved abstract state. Sequential latent variable models (LVMs) present an attractive solution, although existing works either struggle with long-term forecasting or have difficulty learning across diverse dynamics. In this paper, we first present a conceptual framework of sequential LVMs to unify existing works, contrast their fundamental limitations, and identify an intuitive solution to long-term forecasting for diverse dynamics via meta-learning. We then present the first framework of few-shot forecasting for high-dimensional time-series: instead of learning a single dynamic function, we leverage data of diverse dynamics and learn to adapt latent dynamic functions to few-shot support series. This is realized via Bayesian meta-learning underpinned by: 1) a latent dynamic function conditioned on knowledge derived from few-shot support series, and 2) a meta-model that learns to extract such dynamic-specific knowledge via feed-forward embedding of support set. We compared the presented framework with a comprehensive set of baseline models trained 1) globally on the large meta-training set with diverse dynamics, and 2) individually on single dynamics, both with and without fine-tuning to k-shot support series used by the meta-models. We demonstrated that the presented framework is agnostic to the latent dynamic function of choice and, at meta-test time, is able to forecast for new dynamics given variable-shot of support series.
Universal Speech Enhancement with Score-based Diffusion
Authors: Joan Serrà; Santiago Pascual; Jordi Pons; Recep Oguz Araz; Davide Scaini
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Removing background noise from speech audio has been the subject of considerable effort, especially in recent years due to the rise of virtual communication and amateur recordings. Yet background noise is not the only unpleasant disturbance that can prevent intelligibility: reverb, clipping, codec artifacts, problematic equalization, limited bandwidth, or inconsistent loudness are equally disturbing and ubiquitous. In this work, we propose to consider the task of speech enhancement as a holistic endeavor, and present a universal speech enhancement system that tackles 55 different distortions at the same time. Our approach consists of a generative model that employs score-based diffusion, together with a multi-resolution conditioning network that performs enhancement with mixture density networks. We show that this approach significantly outperforms the state of the art in a subjective test performed by expert listeners. We also show that it achieves competitive objective scores with just 4-8 diffusion steps, despite not considering any particular strategy for fast sampling. We hope that both our methodology and technical contributions encourage researchers and practitioners to adopt a universal approach to speech enhancement, possibly framing it as a generative task.
Multi-Modality Alone is Not Enough: Generating Scene Graphs using Cross-Relation-Modality Tokens
Authors: Gopika Sudhakaran; Devendra Singh Dhami; Stefan Roth; Kristian Kersting
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent years have seen a growing interest in Scene Graph Generation (SGG), a comprehensive visual scene understanding task that aims to predict the relationships between objects detected in a scene. One of its key challenges is the strong bias of the visual world around us toward a few frequently occurring relationships, leaving a long tail of under-represented classes. Although infusing additional modalities is one prominent way to improve SGG performance on under-represented classes, we argue that using additional modalities alone is not enough. We propose to inject entity relation information (Cross-Relation) and modality dependencies (Cross-Modality) into each embedding token of a transformer which we term primal fusion. The resulting Cross-RElAtion-Modality (CREAM) token acts as a strong inductive bias for the SGG framework. Our experimental results on the Visual Genome dataset demonstrate that our CREAM model outperforms state-of-the-art SGG models by around 20% while being simpler and requiring substantially less computation. Additionally, to analyse the generalisability of the CREAM model we also evaluate it on the Open Images dataset. Finally, we examine the impact of the depth-map quality on SGG performance and empirically show the superiority of our model over the prior state of the art by better capturing the depth data, boosting the performance by a margin of around 25%.
Feature-Driven Talking Face Generation with StyleGAN2
Authors: Tao Zhang; Kai Tang; Weiwu Zhang; Kazushige Ouchi
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this work, we wish to use a face image that generate a more natural and real face talking animation video. This is not an easy task because face appearance variation and semantics of speech are coupled together when tacking face have a micro movement. Audio features sometimes contain information about expressions, but they are not accurate enough. So a single audio feature cannot fully represent the movement of the face. For the above reason, we want to use different features to generate talking faces. The StyleGan series show good performance in the direction of image processing, and can perform the style migration task of portraits very well at the same time. We find that StyleGan can be used as a talking face generator. At the same time, we also encode and extract non-identity features and non-lip features, and try to find the subtle relationship between the features and the talking face. We also use the evaluation and ablation study to measure the quality of the generated videos and examine whether our approach is effective and feasible.
Sparse tree-based Initialization for Neural Networks
Authors: Patrick Lutz; Ludovic Arnould; Claire Boyer; Erwan Scornet
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Dedicated neural network (NN) architectures have been designed to handle specific data types (such as CNN for images or RNN for text), which ranks them among state-of-the-art methods for dealing with these data. Unfortunately, no architecture has been found for dealing with tabular data yet, for which tree ensemble methods (tree boosting, random forests) usually show the best predictive performances. In this work, we propose a new sparse initialization technique for (potentially deep) multilayer perceptrons (MLP):  we first train a tree-based procedure to detect feature interactions and use the resulting information to initialize the network, which is subsequently trained via standard gradient descent (GD) strategies. Numerical experiments on several tabular data sets showthe benefits of this new, simple and easy-to-use method, both in terms of generalization capacity and computation time, compared to default MLP initialization and even to existing complex deep learning solutions. In fact, this wise MLP initialization raises the performances of the resulting NN methods to that of gradient boosting on tabular data. Besides, such initializations are able to preserve the sparsity of weights introduced in the first layers of the network throughout the training, which emphasizes that the first layers act as a sparse feature extractor (like convolutional layers in CNN).
Variational Information Pursuit for Interpretable Predictions
Authors: Aditya Chattopadhyay; Kwan Ho Ryan Chan; Benjamin David Haeffele; Donald Geman; Rene Vidal
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: There is a growing interest in the machine learning community in developing predictive algorithms that are interpretable by design. To this end, recent work proposes to sequentially ask interpretable queries about data until a high confidence prediction can be made based on the answers obtained (the history). To promote short query-answer chains, a greedy procedure called Information Pursuit (IP) is used, which adaptively chooses queries in order of information gain. Generative models are employed to learn the distribution of query-answers and labels, which is in turn used to estimate the most informative query. However, learning and inference with a full generative model of the data is often intractable for complex tasks. In this work, we propose Variational Information Pursuit (V-IP), a variational characterization of IP which bypasses the need to learn generative models. V-IP is based on finding a query selection strategy and a classifier that minimize the expected cross-entropy between true and predicted labels. We prove that the IP strategy is the optimal solution to this problem. Therefore, instead of learning generative models, we can use our optimal strategy to directly pick the most informative query given any history. We then develop a practical algorithm by defining a finite-dimensional parameterization of our strategy and classifier using deep networks and train them end-to-end using our objective. Empirically, V-IP is 10-100x faster than IP on different Vision and NLP tasks with competitive performance. Moreover, V-IP finds much shorter query chains when compared to reinforcement learning which is typically used in sequential-decision-making problems. Finally, we demonstrate the utility of V-IP on challenging tasks like medical diagnosis where the performance is far superior to the generative modeling approach.
Dateformer: Transformer Extends Look-back Horizon to Predict Longer-term Time Series
Authors: Julong Young; Junhui Chen; Feihu Huang; Jian Peng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Transformers have demonstrated impressive strength in long-term series forecasting. Existing prediction research mostly focused on mapping past short sub-series (lookback window) to future series (forecast window). The longer training dataset time series will be discarded, once training is completed. Models can merely rely on lookback window information for inference, which impedes models from analyzing time series from a global perspective.  And these windows used by Transformers are quite narrow because they must model each time-step therein. Under this point-wise processing style, broadening windows will rapidly exhaust their model capacity. This, for fine-grained time series, leads to a bottleneck in information input and prediction output, which is mortal to long-term series forecasting. To overcome the barrier, we propose a brand-new methodology to use Transformer for time series prediction. Specifically, we split time series into patches by day and reform point-wise to patch-wise processing, which considerably enhances the information input and output of Transformers. To further help models leverage the whole training set's global information during inference, we distill the information, store it in time representations, and replace series with time representations as the main modeling entities. Our designed time-modeling Transformer---Dateformer yields state-of-the-art accuracy on 7 real-world datasets with a 33.6% relative improvement and extends the maximum forecast range to half-year.
Learning Group Importance using the Differentiable Hypergeometric Distribution
Authors: Thomas M. Sutter; Laura Manduchi; Alain Ryser; Julia E Vogt
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Partitioning a set of elements into subsets of a priori unknown sizes is essential in many applications. These subset sizes are rarely explicitly learned - be it the cluster sizes in clustering applications or the number of shared versus independent generative latent factors in weakly-supervised learning. Probability distributions over correct combinations of subset sizes are non-differentiable due to hard constraints, which prohibit gradient-based optimization. In this work, we propose the differentiable hypergeometric distribution. The hypergeometric distribution models the probability of different group sizes based on their relative importance. We introduce reparameterizable gradients to learn the importance between groups and highlight the advantage of explicitly learning the size of subsets in two typical applications: weakly-supervised learning and clustering. In both applications, we outperform previous approaches, which rely on suboptimal heuristics to model the unknown size of groups.
Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation
Authors: Ainesh Bakshi; Piotr Indyk; Praneeth Kacham; Sandeep Silwal; Samson Zhou
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Kernel matrices, as well as weighted graphs represented by them, are ubiquitous objects in machine learning, statistics and other related fields. The main drawback of using kernel methods (learning and inference using kernel matrices) is efficiency -- given $n$ input points, most kernel-based algorithms need to materialize the full $n \times n$ kernel matrix before performing any subsequent computation, thus incurring $\Omega(n^2)$ runtime. Breaking this quadratic barrier for various problems has therefore, been a subject of extensive research efforts. 

We break the quadratic barrier and obtain \emph{subquadratic} time  algorithms for several fundamental linear-algebraic and graph processing primitives, including approximating the top eigenvalue and eigenvector, spectral sparsification, solving linear systems, local clustering, low-rank approximation, arboricity estimation and counting weighted triangles. We build on the recently developed Kernel Density Estimation framework, which (after preprocessing in time subquadratic in $n$) can return estimates of row/column sums of the kernel matrix. In particular, we develop efficient reductions from \emph{weighted vertex} and \emph{weighted edge sampling} on kernel graphs, \emph{simulating random walks} on kernel graphs, and \emph{importance sampling} on matrices to Kernel Density Estimation and show that we can generate samples from these distributions in \emph{sublinear} (in the support of the distribution) time. Our reductions are the central ingredient in each of our applications and we believe they may be of independent interest. We empirically demonstrate the efficacy of our algorithms on low-rank approximation (LRA) and spectral sparsification, where we observe a $\textbf{9x}$ decrease in the number of kernel evaluations over baselines for LRA and a $\textbf{41x}$ reduction in the graph size for spectral sparsification.
Time Series Subsequence Anomaly Detection via Graph Neural Networks
Authors: Weiqi Chen; Zhiqiang Zhou; Qingsong Wen; Liang Sun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Time series subsequence anomaly detection is an important task in a large variety of real-world applications ranging from health monitoring to AIOps, and is challenging due to complicated underlying temporal dynamics and unpredictable anomalous patterns. Firstly, how to effectively learn the temporal dependency in time series remains a challenge. Secondly, diverse and complicated anomalous subsequences as well as the lack of labels make accurate detection difficult. For example, the popular subsequence anomaly detection algorithm---time series discord---fails to handle recurring anomalies. Thirdly, many existing algorithms require a proper subsequence length for effective detection, which is difficult or impossible in practice.  In this paper, we present a novel approach to subsequence anomaly detection which combines practical heuristics of time series discords and temporal relationships with deep neural networks. By performing length selection considering multi-scale information and incorporating prior knowledge using graph neural networks, our method can adaptively learn the appropriate subsequence length as well as integrated representations from both priors and raw data favorable to anomaly detection. In particular, our graph incorporates both semantic and temporal relationships between subsequences. The experimental results demonstrate the effectiveness of the proposed algorithm, which achieves superior performance on multiple time series anomaly benchmarks in comparison with state-of-the-art algorithms.
On the Importance of In-distribution Class Prior for Out-of-distribution Detection
Authors: Xue Jiang; Feng Liu; Zhen Fang; Hong Chen; Tongliang Liu; Feng Zheng; Bo Han
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Given a pre-trained in-distribution (ID) model, the task of inference-time out-of-distribution (OOD) detection methods aims to recognize upcoming OOD data in inference time. However, some representative methods share an unproven assumption that the probability that OOD data belong to every ID class should be the same, i.e., probabilities that OOD data belong to ID classes form a uniform distribution. In this paper, we theoretically and empirically show that this assumption makes these methods incapable of recognizing OOD data when the ID model is trained with class-imbalanced data. Fortunately, by analyzing the causal relations between ID/OOD classes and features, we identify several common scenarios where probabilities that OOD data belong to ID classes should be the ID-class-prior distribution. Based on the above finding, we propose two effective strategies to modify previous inference-time OOD detection methods:  1) if they explicitly use the uniform distribution, we can replace the uniform distribution with the ID-class-prior distribution; 2) otherwise, we can reweight their scores according to the similarity between the ID-class-prior distribution and the softmax outputs of the pre-trained model. Extensive experiments show that both strategies significantly improve the accuracy of recognizing OOD data when the ID model is pre-trained with imbalanced data. As a highlight, when evaluating on the iNaturalist dataset, our method can achieve ~36% increase on AUROC and ~61% decrease on FPR95, compared with the original Energy method, reflecting the importance of ID-class prior in the OOD detection, which lights up a new road to study this problem.
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
Authors: Zhiqi Bu; Yu-Xiang Wang; Sheng Zha; George Karypis
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Per-example gradient clipping is a key algorithmic step that enables practical differential private (DP) training for deep learning models. The choice of clipping threshold $R$, however, is shown to be vital for achieving high accuracy under DP. We propose an easy-to-use replacement, called automatic clipping, that eliminates the need to tune $R$ for any DP optimizers, including DP-SGD, DP-Adam, DP-LAMB and many others.
The automatic variants are as private and computationally efficient as existing DP optimizers, but require no DP-specific hyperparameters and thus make DP training as amenable as the standard non-private training. We give a rigorous convergence analysis of automatic DP-SGD in the non-convex setting, which shows that it {can enjoy an asymptotic convergence rate that matches the standard SGD, under a symmetric gradient noise assumption of the per-sample gradients.} We also demonstrate on various language and vision tasks that automatic clipping outperforms or matches the state-of-the-art, and can be easily employed with minimal changes to existing codebases.