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Title: Few-Shot Unlearning by Model Inversion Abstract: We consider the problem of machine unlearning to erase a target dataset, which causes an unwanted behavior, from the trained model when the training dataset is not given. Previous works have assumed that the target dataset indicates all the training data imposing ... |
Title: HW-Aware Initialization of DNN Auto-Tuning to Improve Exploration Time and Robustness Abstract: The process of optimizing the latency of DNN operators with ML models and hardware-in-the-loop, called auto-tuning, has established itself as a pervasive method for the deployment of neural networks. From a search spa... |
Title: GSR: A Generalized Symbolic Regression Approach Abstract: Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR). In contrast to neural networks which are often treated as black boxes, SR attempts to... |
Title: A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting Abstract: We present a new method that includes three key components of distributed optimization and federated learning: variance reduction of stochastic gradients, compressed communication, a... |
Title: Comparing interpretation methods in mental state decoding analyses with deep learning models Abstract: Deep learning (DL) methods find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (such as accepting or rejecting a gamble) and brain activi... |
Title: Semantic Autoencoder and Its Potential Usage for Adversarial Attack Abstract: Autoencoder can give rise to an appropriate latent representation of the input data, however, the representation which is solely based on the intrinsic property of the input data, is usually inferior to express some semantic informatio... |
Title: Individual health-disease phase diagrams for disease prevention based on machine learning Abstract: Early disease detection and prevention methods based on effective interventions are gaining attention. Machine learning technology has enabled precise disease prediction by capturing individual differences in mult... |
Title: GlanceNets: Interpretabile, Leak-proof Concept-based Models Abstract: There is growing interest in concept-based models (CBMs) that combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A key requirement is that the concepts be interpretable. Existing ... |
Title: Communication-Efficient Distributionally Robust Decentralized Learning Abstract: Decentralized learning algorithms empower interconnected edge devices to share data and computational resources to collaboratively train a machine learning model without the aid of a central coordinator (e.g. an orchestrating basest... |
Title: Optimizing Intermediate Representations of Generative Models for Phase Retrieval Abstract: Phase retrieval is the problem of reconstructing images from magnitude-only measurements. In many real-world applications the problem is underdetermined. When training data is available, generative models are a new idea to... |
Title: Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks Abstract: Few-shot learning for neural networks (NNs) is an important problem that aims to train NNs with a few data. The main challenge is how to avoid overfitting since over-parameterized NNs can easily o... |
Title: k-Means Maximum Entropy Exploration Abstract: Exploration in high-dimensional, continuous spaces with sparse rewards is an open problem in reinforcement learning. Artificial curiosity algorithms address this by creating rewards that lead to exploration. Given a reinforcement learning algorithm capable of maximiz... |
Title: Scalable Distributional Robustness in a Class of Non Convex Optimization with Guarantees Abstract: Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of frac... |
Title: Differentiable Invariant Causal Discovery Abstract: Learning causal structure from observational data is a fundamental challenge in machine learning. The majority of commonly used differentiable causal discovery methods are non-identifiable, turning this problem into a continuous optimization task prone to data ... |
Title: Label-Enhanced Graph Neural Network for Semi-supervised Node Classification Abstract: Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNN... |
Title: Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems Abstract: Model-free deep-reinforcement-based learning algorithms have been applied to a range of COPs~\cite{bello2016neural}~\cite{kool2018attention}~\cite{nazari2018reinforcement}. However, these approaches suffer from two key challen... |
Title: The CLRS Algorithmic Reasoning Benchmark Abstract: Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms. Several important works have investigated whether neural networks can effectively reason like algorithms, t... |
Title: Multi-task Optimization Based Co-training for Electricity Consumption Prediction Abstract: Real-world electricity consumption prediction may involve different tasks, e.g., prediction for different time steps ahead or different geo-locations. These tasks are often solved independently without utilizing some commo... |
Title: Generalised Implicit Neural Representations Abstract: We consider the problem of learning implicit neural representations (INRs) for signals on non-Euclidean domains. In the Euclidean case, INRs are trained on a discrete sampling of a signal over a regular lattice. Here, we assume that the continuous signal exis... |
Title: Contrastive Representation Learning for 3D Protein Structures Abstract: Learning from 3D protein structures has gained wide interest in protein modeling and structural bioinformatics. Unfortunately, the number of available structures is orders of magnitude lower than the training data sizes commonly used in comp... |
Title: Augmentation-Aware Self-Supervision for Data-Efficient GAN Training Abstract: Training generative adversarial networks (GANs) with limited data is valuable but challenging because discriminators are prone to over-fitting in such situations. Recently proposed differentiable data augmentation techniques for discri... |
Title: Automatic Relation-aware Graph Network Proliferation Abstract: Graph neural architecture search has sparked much attention as Graph Neural Networks (GNNs) have shown powerful reasoning capability in many relational tasks. However, the currently used graph search space overemphasizes learning node features and ne... |
Title: Simulation-Based Inference with WALDO: Perfectly Calibrated Confidence Regions Using Any Prediction or Posterior Estimation Algorithm Abstract: The vast majority of modern machine learning targets prediction problems, with algorithms such as Deep Neural Networks revolutionizing the accuracy of point predictions ... |
Title: Static Scheduling with Predictions Learned through Efficient Exploration Abstract: A popular approach to go beyond the worst-case analysis of online algorithms is to assume the existence of predictions that can be leveraged to improve performances. Those predictions are usually given by some external sources tha... |
Title: A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groups Abstract: In this paper, we introduce a novel methodology to model rating transitions with a stochastic process. To introduce stochastic processes, whose values are valid rating matrices, we noticed the geometric properti... |
Title: Provable General Function Class Representation Learning in Multitask Bandits and MDPs Abstract: While multitask representation learning has become a popular approach in reinforcement learning (RL) to boost the sample efficiency, the theoretical understanding of why and how it works is still limited. Most previou... |
Title: Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning Abstract: This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian pro... |
Title: Mitigating Dataset Bias by Using Per-sample Gradient Abstract: The performance of deep neural networks is strongly influenced by the training dataset setup. In particular, when attributes having a strong correlation with the target attribute are present, the trained model can provide unintended prejudgments and ... |
Title: Multilingual Transformers for Product Matching -- Experiments and a New Benchmark in Polish Abstract: Product matching corresponds to the task of matching identical products across different data sources. It typically employs available product features which, apart from being multimodal, i.e., comprised of vario... |
Title: Multi-Agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP Abstract: Factored decentralized Markov decision process (Dec-MDP) is a framework for modeling sequential decision making problems in multi-agent systems. In this paper, we formalize the learning of numerical methods for hyperbol... |
Title: One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching Abstract: Image hashing is a principled approximate nearest neighbor approach to find similar items to a query in a large collection of images. Hashing aims to learn a binary-output function that maps an image to a binary v... |
Title: Transformers for Multi-Object Tracking on Point Clouds Abstract: We present TransMOT, a novel transformer-based end-to-end trainable online tracker and detector for point cloud data. The model utilizes a cross- and a self-attention mechanism and is applicable to lidar data in an automotive context, as well as ot... |
Title: ViNNPruner: Visual Interactive Pruning for Deep Learning Abstract: Neural networks grow vastly in size to tackle more sophisticated tasks. In many cases, such large networks are not deployable on particular hardware and need to be reduced in size. Pruning techniques help to shrink deep neural networks to smaller... |
Title: Template based Graph Neural Network with Optimal Transport Distances Abstract: Current Graph Neural Networks (GNN) architectures generally rely on two important components: node features embedding through message passing, and aggregation with a specialized form of pooling. The structural (or topological) informa... |
Title: Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks Abstract: Automatic modulation classification (AMC) using the Deep Neural Network (DNN) approach outperforms the traditional classification techniques, even in the presence of challenging wireless channel environments. However, the ... |
Title: HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks Abstract: The aim of Few-Shot learning methods is to train models which can easily adapt to previously unseen tasks, based on small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic Meta-Learning (M... |
Title: Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning Abstract: Unsupervised/self-supervised graph representation learning is critical for downstream node- and graph-level classification tasks. Global structure of graphs helps discriminating representations and existing methods... |
Title: Adversarial synthesis based data-augmentation for code-switched spoken language identification Abstract: Spoken Language Identification (LID) is an important sub-task of Automatic Speech Recognition(ASR) that is used to classify the language(s) in an audio segment. Automatic LID plays an useful role in multiling... |
Title: Non-Iterative Recovery from Nonlinear Observations using Generative Models Abstract: In this paper, we aim to estimate the direction of an underlying signal from its nonlinear observations following the semi-parametric single index model (SIM). Unlike conventional compressed sensing where the signal is assumed t... |
Title: Variable importance without impossible data Abstract: The most popular methods for measuring importance of the variables in a black box prediction algorithm make use of synthetic inputs that combine predictor variables from multiple subjects. These inputs can be unlikely, physically impossible, or even logically... |
Title: Hierarchies of Reward Machines Abstract: Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode landmarks of the task using high-level events. The structure of RMs enables the decomposition of a task int... |
Title: Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark Abstract: Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on i... |
Title: Knowledge Enhanced Neural Networks for relational domains Abstract: In the recent past, there has been a growing interest in Neural-Symbolic Integration frameworks, i.e., hybrid systems that integrate connectionist and symbolic approaches to obtain the best of both worlds. In this work we focus on a specific met... |
Title: Exact Feature Collisions in Neural Networks Abstract: Predictions made by deep neural networks were shown to be highly sensitive to small changes made in the input space where such maliciously crafted data points containing small perturbations are being referred to as adversarial examples. On the other hand, rec... |
Title: SymFormer: End-to-end symbolic regression using transformer-based architecture Abstract: Many real-world problems can be naturally described by mathematical formulas. The task of finding formulas from a set of observed inputs and outputs is called symbolic regression. Recently, neural networks have been applied ... |
Title: Strategic Classification with Graph Neural Networks Abstract: Strategic classification studies learning in settings where users can modify their features to obtain favorable predictions. Most current works focus on simple classifiers that trigger independent user responses. Here we examine the implications of le... |
Title: SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections Abstract: Inverse rendering of an object under entirely unknown capture conditions is a fundamental challenge in computer vision and graphics. Neural approaches such as NeRF have achieved photorealistic results on novel view sy... |
Title: Concept-level Debugging of Part-Prototype Networks Abstract: Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency. ProtoPNets compute predictions based on similarity to class-specific part-prototypes lear... |
Title: Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization Abstract: Bayesian Likelihood-Free Inference methods yield posterior approximations for simulator models with intractable likelihood. Recently, many works trained neural networks to approximate either the intractable likeliho... |
Title: A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud Abstract: Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud. In recent work... |
Title: AdaTask: Adaptive Multitask Online Learning Abstract: We introduce and analyze AdaTask, a multitask online learning algorithm that adapts to the unknown structure of the tasks. When the $N$ tasks are stochastically activated, we show that the regret of AdaTask is better, by a factor that can be as large as $\sqr... |
Title: Feature Learning in $L_{2}$-regularized DNNs: Attraction/Repulsion and Sparsity Abstract: We study the loss surface of DNNs with $L_{2}$ regularization. We show that the loss in terms of the parameters can be reformulated into a loss in terms of the layerwise activations $Z_{\ell}$ of the training set. This refo... |
Title: Unsupervised Image Representation Learning with Deep Latent Particles Abstract: We propose a new representation of visual data that disentangles object position from appearance. Our method, termed Deep Latent Particles (DLP), decomposes the visual input into low-dimensional latent ``particles'', where each parti... |
Title: Graph Backup: Data Efficient Backup Exploiting Markovian Transitions Abstract: The successes of deep Reinforcement Learning (RL) are limited to settings where we have a large stream of online experiences, but applying RL in the data-efficient setting with limited access to online interactions is still challengin... |
Title: Robust Anytime Learning of Markov Decision Processes Abstract: Markov decision processes (MDPs) are formal models commonly used in sequential decision-making. MDPs capture the stochasticity that may arise, for instance, from imprecise actuators via probabilities in the transition function. However, in data-drive... |
Title: Attribution-based Explanations that Provide Recourse Cannot be Robust Abstract: Different users of machine learning methods require different explanations, depending on their goals. To make machine learning accountable to society, one important goal is to get actionable options for recourse, which allow an affec... |
Title: Surface Analysis with Vision Transformers Abstract: The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design limitations resulting in poor modelling of long-range associations, as the generalis... |
Title: Predicting Day-Ahead Stock Returns using Search Engine Query Volumes: An Application of Gradient Boosted Decision Trees to the S&P 100 Abstract: The internet has changed the way we live, work and take decisions. As it is the major modern resource for research, detailed data on internet usage exhibits vast amount... |
Title: coVariance Neural Networks Abstract: Graph neural networks (GNN) are an effective framework that exploit inter-relationships within graph-structured data for learning. Principal component analysis (PCA) involves the projection of data on the eigenspace of the covariance matrix and draws similarities with the gra... |
Title: Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression Abstract: Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been... |
Title: A Reduction to Binary Approach for Debiasing Multiclass Datasets Abstract: We propose a novel reduction-to-binary (R2B) approach that enforces demographic parity for multiclass classification with non-binary sensitive attributes via a reduction to a sequence of binary debiasing tasks. We prove that R2B satisfies... |
Title: Snapture -- A Novel Neural Architecture for Combined Static and Dynamic Hand Gesture Recognition Abstract: As robots are expected to get more involved in people's everyday lives, frameworks that enable intuitive user interfaces are in demand. Hand gesture recognition systems provide a natural way of communicatio... |
Title: CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers Abstract: Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation. Its application to video generation is still facing many challenges: The potential huge computat... |
Title: SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series Abstract: Continuous monitoring with an ever-increasing number of sensors has become ubiquitous across many application domains. Acquired data are typically high-dimensional and difficult ... |
Title: Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games Abstract: Learning to play optimally against any mixture over a diverse set of strategies is of important practical interests in competitive games. In this paper, we propose simplex-NeuPL that satisfies two desiderata si... |
Title: Compressed Hierarchical Representations for Multi-Task Learning and Task Clustering Abstract: In this paper, we frame homogeneous-feature multi-task learning (MTL) as a hierarchical representation learning problem, with one task-agnostic and multiple task-specific latent representations. Drawing inspiration from... |
Title: One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning Abstract: While parallelism has been extensively used in Reinforcement Learning (RL), the quantitative effects of parallel exploration are not well understood theoretically. We study the bene... |
Title: VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting Abstract: Time series models aim for accurate predictions of the future given the past, where the forecasts are used for important downstream tasks like business decision making. In practice, deep learning based time series models come ... |
Title: FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy Abstract: Node embedding aims to map nodes in the complex graph into low-dimensional representations. The real-world large-scale graphs and difficulties of labeling motivate wide studies of unsupervised node embeddin... |
Title: Learning brain MRI quality control: a multi-factorial generalization problem Abstract: Due to the growing number of MRI data, automated quality control (QC) has become essential, especially for larger scale analysis. Several attempts have been made in order to develop reliable and scalable QC pipelines. However,... |
Title: Variational inference via Wasserstein gradient flows Abstract: Along with Markov chain Monte Carlo (MCMC) methods, variational inference (VI) has emerged as a central computational approach to large-scale Bayesian inference. Rather than sampling from the true posterior $\pi$, VI aims at producing a simple but ef... |
Title: Online Meta-Learning in Adversarial Multi-Armed Bandits Abstract: We study meta-learning for adversarial multi-armed bandits. We consider the online-within-online setup, in which a player (learner) encounters a sequence of multi-armed bandit episodes. The player's performance is measured as regret against the be... |
Title: Continuous Temporal Graph Networks for Event-Based Graph Data Abstract: There has been an increasing interest in modeling continuous-time dynamics of temporal graph data. Previous methods encode time-evolving relational information into a low-dimensional representation by specifying discrete layers of neural net... |
Title: Inducing bias is simpler than you think Abstract: Machine learning may be oblivious to human bias but it is not immune to its perpetuation. Marginalisation and iniquitous group representation are often traceable in the very data used for training, and may be reflected or even enhanced by the learning models. To ... |
Title: Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees Abstract: The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift account... |
Title: Hide and Seek: on the Stealthiness of Attacks against Deep Learning Systems Abstract: With the growing popularity of artificial intelligence and machine learning, a wide spectrum of attacks against deep learning models have been proposed in the literature. Both the evasion attacks and the poisoning attacks attem... |
Title: Evaluating Robustness to Dataset Shift via Parametric Robustness Sets Abstract: We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance. To ensure that these shifts are plausible, we parameterize them in terms of interpretable chan... |
Title: Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues Abstract: Movies reflect society and also hold power to transform opinions. Social biases and stereotypes present in movies can cause extensive damage due to their reach. These biases are not always found to be the need of story... |
Title: Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain Abstract: In the commercial aviation domain, there are a large number of documents, like, accident reports (NTSB, ASRS) and regulatory directives (ADs). There is a need for a system to access these diverse repositories ... |
Title: Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints Abstract: Many real-world settings involve costs for performing actions; transaction costs in financial systems and fuel costs being common examples. In these settings, performing actions at each time step quickly acc... |
Title: You Can't Count on Luck: Why Decision Transformers Fail in Stochastic Environments Abstract: Recently, methods such as Decision Transformer that reduce reinforcement learning to a prediction task and solve it via supervised learning (RvS) have become popular due to their simplicity, robustness to hyperparameters... |
Title: FedHarmony: Unlearning Scanner Bias with Distributed Data Abstract: The ability to combine data across scanners and studies is vital for neuroimaging, to increase both statistical power and the representation of biological variability. However, combining datasets across sites leads to two challenges: first, an i... |
Title: Semi-Supervised Cross-Silo Advertising with Partial Knowledge Transfer Abstract: As an emerging secure learning paradigm in leveraging cross-agency private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately o... |
Title: A deep learning approach to halo merger tree construction Abstract: A key ingredient for semi-analytic models (SAMs) of galaxy formation is the mass assembly history of haloes, encoded in a tree structure. The most commonly used method to construct halo merger histories is based on the outcomes of high-resolutio... |
Title: Private Federated Submodel Learning with Sparsification Abstract: We investigate the problem of private read update write (PRUW) in federated submodel learning (FSL) with sparsification. In FSL, a machine learning model is divided into multiple submodels, where each user updates only the submodel that is relevan... |
Title: TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving Abstract: How should we integrate representations from complementary sensors for autonomous driving? Geometry-based fusion has shown promise for perception (e.g. object detection, motion forecasting). However, in the context of end... |
Title: Learning (Very) Simple Generative Models Is Hard Abstract: Motivated by the recent empirical successes of deep generative models, we study the computational complexity of the following unsupervised learning problem. For an unknown neural network $F:\mathbb{R}^d\to\mathbb{R}^{d'}$, let $D$ be the distribution ove... |
Title: What Knowledge Gets Distilled in Knowledge Distillation? Abstract: Knowledge distillation aims to transfer useful information from a teacher network to a student network, with the primary goal of improving the student's performance for the task at hand. Over the years, there has a been a deluge of novel techniqu... |
Title: Weight Set Decomposition for Weighted Rank Aggregation: An interpretable and visual decision support tool Abstract: The problem of interpreting or aggregating multiple rankings is common to many real-world applications. Perhaps the simplest and most common approach is a weighted rank aggregation, wherein a (conv... |
Title: Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful i... |
Title: Are classical neural networks quantum? Abstract: Neural networks are being used to improve the probing of the state spaces of many particle systems as approximations to wavefunctions and in order to avoid the recurring sign problem of quantum monte-carlo. One may ask whether the usual classical neural networks h... |
Title: COIN: Co-Cluster Infomax for Bipartite Graphs Abstract: Bipartite graphs are powerful data structures to model interactions between two types of nodes, which have been used in a variety of applications, such as recommender systems, information retrieval, and drug discovery. A fundamental challenge for bipartite ... |
Title: A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling Abstract: Data insufficiency problem (i.e., data missing and label scarcity issues) caused by inadequate services and infrastructures or unbalanced development levels of cities has seriously affected the urban computing ta... |
Title: Online PAC-Bayes Learning Abstract: Most PAC-Bayesian bounds hold in the batch learning setting where data is collected at once, prior to inference or prediction. This somewhat departs from many contemporary learning problems where data streams are collected and the algorithms must dynamically adjust. We prove n... |
Title: Evolving Domain Generalization Abstract: Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data. Existing domain generalization methods ignore the relationship between tasks, i... |
Title: PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs Abstract: Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spac... |
Title: FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation Abstract: The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions. A common approach to quan... |
Title: Learning Instance-Specific Data Augmentations Abstract: Existing data augmentation methods typically assume independence between transformations and inputs: they use the same transformation distribution for all input instances. We explain why this can be problematic and propose InstaAug, a method for automatical... |
Title: Asynchronous Hierarchical Federated Learning Abstract: Federated Learning is a rapidly growing area of research and with various benefits and industry applications. Typical federated patterns have some intrinsic issues such as heavy server traffic, long periods of convergence, and unreliable accuracy. In this pa... |
Title: Distributed Graph Neural Network Training with Periodic Historical Embedding Synchronization Abstract: Despite the recent success of Graph Neural Networks (GNNs), it remains challenging to train a GNN on large graphs, which are prevalent in various applications such as social network, recommender systems, and kn... |
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