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Title: Recommender Transformers with Behavior Pathways Abstract: Sequential recommendation requires the recommender to capture the evolving behavior characteristics from logged user behavior data for accurate recommendations. However, user behavior sequences are viewed as a script with multiple ongoing threads intertwi... |
Title: Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds Abstract: This paper considers the multi-armed bandit (MAB) problem and provides a new best-of-both-worlds (BOBW) algorithm that works nearly optimally in both stochastic and adversarial settings. In stochastic settings, some... |
Title: Adversarial Audio Synthesis with Complex-valued Polynomial Networks Abstract: Time-frequency (TF) representations in audio synthesis have been increasingly modeled with real-valued networks. However, overlooking the complex-valued nature of TF representations can result in suboptimal performance and require addi... |
Title: Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites Abstract: In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storag... |
Title: Physics-Informed Transfer Learning Strategy to Accelerate Unsteady Fluid Flow Simulations Abstract: Since the derivation of the Navier Stokes equations, it has become possible to numerically solve real world viscous flow problems (computational fluid dynamics (CFD)). However, despite the rapid advancements in th... |
Title: Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring Abstract: Attributes skew hinders the current federated learning (FL) frameworks from consistent optimization directions among the clients, which inevitably leads to performance reduction and unstabl... |
Title: Tailored max-out networks for learning convex PWQ functions Abstract: Convex piecewise quadratic (PWQ) functions frequently appear in control and elsewhere. For instance, it is well-known that the optimal value function (OVF) as well as Q-functions for linear MPC are convex PWQ functions. Now, in learning-based ... |
Title: Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization Abstract: Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimate. Several procedures were proposed to r... |
Title: Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception Abstract: Data-driven models (DDM) based on machine learning and other AI techniques play an important role in the perception of increasingly autonomous systems. Due to the merely implicit definition of th... |
Title: Robust Reinforcement Learning with Distributional Risk-averse formulation Abstract: Robust Reinforcement Learning tries to make predictions more robust to changes in the dynamics or rewards of the system. This problem is particularly important when the dynamics and rewards of the environment are estimated from t... |
Title: On the Finite-Time Performance of the Knowledge Gradient Algorithm Abstract: The knowledge gradient (KG) algorithm is a popular and effective algorithm for the best arm identification (BAI) problem. Due to the complex calculation of KG, theoretical analysis of this algorithm is difficult, and existing results ar... |
Title: When adversarial attacks become interpretable counterfactual explanations Abstract: We argue that, when learning a 1-Lipschitz neural network with the dual loss of an optimal transportation problem, the gradient of the model is both the direction of the transportation plan and the direction to the closest advers... |
Title: Evaluating histopathology transfer learning with ChampKit Abstract: Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection ... |
Title: How are policy gradient methods affected by the limits of control? Abstract: We study stochastic policy gradient methods from the perspective of control-theoretic limitations. Our main result is that ill-conditioned linear systems in the sense of Doyle inevitably lead to noisy gradient estimates. We also give an... |
Title: On Provably Robust Meta-Bayesian Optimization Abstract: Bayesian optimization (BO) has become popular for sequential optimization of black-box functions. When BO is used to optimize a target function, we often have access to previous evaluations of potentially related functions. This begs the question as to whet... |
Title: Temporal Multimodal Multivariate Learning Abstract: We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observations of a probability distribution with more than one peak or more than one outcom... |
Title: Neural interval-censored Cox regression with feature selection Abstract: The classical Cox model emerged in 1972 promoting breakthroughs in how patient prognosis is quantified using time-to-event analysis in biomedicine. One of the most useful characteristics of the model for practitioners is the interpretabilit... |
Title: Grad-GradaGrad? A Non-Monotone Adaptive Stochastic Gradient Method Abstract: The classical AdaGrad method adapts the learning rate by dividing by the square root of a sum of squared gradients. Because this sum on the denominator is increasing, the method can only decrease step sizes over time, and requires a lea... |
Title: A Local Optima Network Analysis of the Feedforward Neural Architecture Space Abstract: This study investigates the use of local optima network (LON) analysis, a derivative of the fitness landscape of candidate solutions, to characterise and visualise the neural architecture space. The search space of feedforward... |
Title: Manifold Alignment-Based Multi-Fidelity Reduced-Order Modeling Applied to Structural Analysis Abstract: This work presents the application of a recently developed parametric, non-intrusive, and multi-fidelity reduced-order modeling method on high-dimensional displacement and stress fields arising from the struct... |
Title: Object Scene Representation Transformer Abstract: A compositional understanding of the world in terms of objects and their geometry in 3D space is considered a cornerstone of human cognition. Facilitating the learning of such a representation in neural networks holds promise for substantially improving labeled d... |
Title: Scaling ResNets in the Large-depth Regime Abstract: Deep ResNets are recognized for achieving state-of-the-art results in complex machine learning tasks. However, the remarkable performance of these architectures relies on a training procedure that needs to be carefully crafted to avoid vanishing or exploding gr... |
Title: Monitoring Urban Forests from Auto-Generated Segmentation Maps Abstract: We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interaction. Successfully training machine learning models for sema... |
Title: FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents Abstract: Unstructured data, especially text, continues to grow rapidly in various domains. In particular, in the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosu... |
Title: Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models Abstract: Predictive machine learning models nowadays are often updated in a stateless and expensive way. The two main future trends for companies that want to build machine learning-based applications and systems are re... |
Title: AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data Abstract: Semi-supervised learning (SSL) has seen great strides when labeled data is scarce but unlabeled data is abundant. Critically, most recent work assume that such unlabeled data is drawn from the same distribution as the labeled data. In t... |
Title: ABCinML: Anticipatory Bias Correction in Machine Learning Applications Abstract: The idealization of a static machine-learned model, trained once and deployed forever, is not practical. As input distributions change over time, the model will not only lose accuracy, any constraints to reduce bias against a protec... |
Title: Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch Abstract: Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-the-art handcrafted heuristic strategies suffer from ... |
Title: Highly Efficient Structural Learning of Sparse Staged Trees Abstract: Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined. However, they do not scale efficiently as the number of variables considered increases. Here we introduce the first... |
Title: Two-terminal source coding with common sum reconstruction Abstract: We present the problem of two-terminal source coding with Common Sum Reconstruction (CSR). Consider two terminals, each with access to one of two correlated sources. Both terminals want to reconstruct the sum of the two sources under some averag... |
Title: DeepTPI: Test Point Insertion with Deep Reinforcement Learning Abstract: Test point insertion (TPI) is a widely used technique for testability enhancement, especially for logic built-in self-test (LBIST) due to its relatively low fault coverage. In this paper, we propose a novel TPI approach based on deep reinfo... |
Title: Resource Allocation for Compression-aided Federated Learning with High Distortion Rate Abstract: Recently, a considerable amount of works have been made to tackle the communication burden in federated learning (FL) (e.g., model quantization, data sparsification, and model compression). However, the existing meth... |
Title: Edge Graph Neural Networks for Massive MIMO Detection Abstract: Massive Multiple-Input Multiple-Out (MIMO) detection is an important problem in modern wireless communication systems. While traditional Belief Propagation (BP) detectors perform poorly on loopy graphs, the recent Graph Neural Networks (GNNs)-based ... |
Title: Exploring Representation of Horn Clauses using GNNs Abstract: Learning program semantics from raw source code is challenging due to the complexity of real-world programming language syntax and due to the difficulty of reconstructing long-distance relational information implicitly represented in programs using id... |
Title: SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction Abstract: Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good repr... |
Title: Federated Optimization Algorithms with Random Reshuffling and Gradient Compression Abstract: Gradient compression is a popular technique for improving communication complexity of stochastic first-order methods in distributed training of machine learning models. However, the existing works consider only with-repl... |
Title: Learning Behavior Representations Through Multi-Timescale Bootstrapping Abstract: Natural behavior consists of dynamics that are both unpredictable, can switch suddenly, and unfold over many different timescales. While some success has been found in building representations of behavior under constrained or simpl... |
Title: ReCo: Retrieve and Co-segment for Zero-shot Transfer Abstract: Semantic segmentation has a broad range of applications, but its real-world impact has been significantly limited by the prohibitive annotation costs necessary to enable deployment. Segmentation methods that forgo supervision can side-step these cost... |
Title: A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing Abstract: Knowledge of mixtures' phase equilibria is crucial in nature and technical chemistry. Phase equilibria calculations of mixtures require activity coefficients. However, experimental data on ac... |
Title: Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning Abstract: This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy? Positive evidence is provided for the first t... |
Title: Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience Abstract: Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called generative methods, which genera... |
Title: Learning the Structure of Large Networked Systems Obeying Conservation Laws Abstract: Many networked systems such as electric networks, the brain, and social networks of opinion dynamics are known to obey conservation laws. Examples of this phenomenon include the Kirchoff laws in electric networks and opinion co... |
Title: Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction Abstract: Normalization layers (e.g., Batch Normalization, Layer Normalization) were introduced to help with optimization difficulties in very deep nets, but they clearly also help generalization, even in not-so-deep nets. Moti... |
Title: Combining Counterfactuals With Shapley Values To Explain Image Models Abstract: With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task. Models trained on tabular data have witnessed significant progress in expla... |
Title: A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus Abstract: In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners. ... |
Title: Inverse design of nano-photonic wavelength demultiplexer with a deep neural network approach Abstract: In this paper, we propose a pre-trained-combined neural network (PTCN) as a comprehensive solution to the inverse design of an integrated photonic circuit. By utilizing both the initially pre-trained inverse an... |
Title: Loss Functions for Classification using Structured Entropy Abstract: Cross-entropy loss is the standard metric used to train classification models in deep learning and gradient boosting. It is well-known that this loss function fails to account for similarities between the different values of the target. We prop... |
Title: Lazy Queries Can Reduce Variance in Zeroth-order Optimization Abstract: A major challenge of applying zeroth-order (ZO) methods is the high query complexity, especially when queries are costly. We propose a novel gradient estimation technique for ZO methods based on adaptive lazy queries that we term as LAZO. Di... |
Title: Stability of image reconstruction algorithms Abstract: Robustness and stability of image reconstruction algorithms have recently come under scrutiny. Their importance to medical imaging cannot be overstated. We review the known results for the topical variational regularization strategies ($\ell_2$ and $\ell_1$ ... |
Title: Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger 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 norm $R$, however, is shown to be vital for achieving high... |
Title: Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt Abstract: Training on web-scale data can take months. But most computation and time is wasted on redundant and noisy points that are already learnt or not learnable. To accelerate training, we introduce Reducible Holdout Loss S... |
Title: MBGDT:Robust Mini-Batch Gradient Descent Abstract: In high dimensions, most machine learning method perform fragile even there are a little outliers. To address this, we hope to introduce a new method with the base learner, such as Bayesian regression or stochastic gradient descent to solve the problem of the vu... |
Title: Flatten the Curve: Efficiently Training Low-Curvature Neural Networks Abstract: The highly non-linear nature of deep neural networks causes them to be susceptible to adversarial examples and have unstable gradients which hinders interpretability. However, existing methods to solve these issues, such as adversari... |
Title: An Intelligent Assistant for Converting City Requirements to Formal Specification Abstract: As more and more monitoring systems have been deployed to smart cities, there comes a higher demand for converting new human-specified requirements to machine-understandable formal specifications automatically. However, t... |
Title: Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features Abstract: Deep learning models trained in a fully supervised manner have been shown to rely on so-called "shortcut" features. Shortcut features are inputs that are associated with the outcome of interest in the training data, but ar... |
Title: GraphFM: Improving Large-Scale GNN Training via Feature Momentum Abstract: Training of graph neural networks (GNNs) for large-scale node classification is challenging. A key difficulty lies in obtaining accurate hidden node representations while avoiding the neighborhood explosion problem. Here, we propose a new... |
Title: Category-Agnostic 6D Pose Estimation with Conditional Neural Processes Abstract: We present a novel meta-learning approach for 6D pose estimation on unknown objects. In contrast to "instance-level" pose estimation methods, our algorithm learns object representation in a category-agnostic way, which endows it wit... |
Title: DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method Abstract: Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. Howeve... |
Title: Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learning from static datasets, without interacting with the underlying environment during the learning proc... |
Title: Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design Abstract: Deep Generative Machine Learning Models (DGMs) have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. DGMs are conventionally trained to minimiz... |
Title: Proximal Splitting Adversarial Attacks for Semantic Segmentation Abstract: Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation. The methods proposed in these works do not accurately s... |
Title: To Aggregate or Not? Learning with Separate Noisy Labels Abstract: The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing). Typically one would first aggregate the separate noisy labels into one and apply standard training me... |
Title: Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising Abstract: Neural network policies trained using Deep Reinforcement Learning (DRL) are well-known to be susceptible to adversarial attacks. In this paper, we consider attacks manifesting as perturbations in the observation sp... |
Title: Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework Abstract: In this paper we describe our work towards building a generic framework for both multi-modal embedding and multi-label binary classification tasks, while participating in task 5 (Multimedia Automatic Mi... |
Title: Machines Explaining Linear Programs Abstract: There has been a recent push in making machine learning models more interpretable so that their performance can be trusted. Although successful, these methods have mostly focused on the deep learning methods while the fundamental optimization methods in machine learn... |
Title: Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation Abstract: Simulations are ubiquitous in machine learning. Especially in graph learning, simulations of Directed Acyclic Graphs (DAG) are being deployed for evaluating new algorithms. In the literature, it was recently argued that c... |
Title: Towards a Solution to Bongard Problems: A Causal Approach Abstract: To date, Bongard Problems (BP) remain one of the few fortresses of AI history yet to be raided by the powerful models of the current era. We present a systematic analysis using modern techniques from the intersection of causality and AI/ML in a ... |
Title: Improving Solar Flare Prediction by Time Series Outlier Detection Abstract: Solar flares not only pose risks to outer space technologies and astronauts' well being, but also cause disruptions on earth to our hight-tech, interconnected infrastructure our lives highly depend on. While a number of machine-learning ... |
Title: Benefits of Additive Noise in Composing Classes with Bounded Capacity Abstract: We observe that given two (compatible) classes of functions $\mathcal{F}$ and $\mathcal{H}$ with small capacity as measured by their uniform covering numbers, the capacity of the composition class $\mathcal{H} \circ \mathcal{F}$ can ... |
Title: Using Machine Learning to Augment Dynamic Time Warping Based Signal Classification Abstract: Modern applications such as voice recognition rely on the ability to compare signals to pre-recorded ones to classify them. However, this comparison typically needs to ignore differences due to signal noise, temporal off... |
Title: Attributions Beyond Neural Networks: The Linear Program Case Abstract: Linear Programs (LPs) have been one of the building blocks in machine learning and have championed recent strides in differentiable optimizers for learning systems. While there exist solvers for even high-dimensional LPs, understanding said h... |
Title: Explainable expected goal models for performance analysis in football analytics Abstract: The expected goal provides a more representative measure of the team and player performance which also suit the low-scoring nature of football instead of score in modern football. The score of a match involves randomness an... |
Title: A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects Abstract: The recent development of deep learning combined with compressed sensing enables fast reconstruction of undersampled MR images and has achieved state-of-the-art performance for Carte... |
Title: Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning Abstract: Emotion classification of speech and assessment of the emotion strength are required in applications such as emotional text-to-speech and voice conversion. The emotion attribute ranking function based on ... |
Title: Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints Abstract: There is a disconnect between how researchers and practitioners handle privacy-utility tradeoffs. Researchers primarily operate from a privacy first perspective, setting strict privacy requirements and minimizing risk subject ... |
Title: Training Discrete Deep Generative Models via Gapped Straight-Through Estimator Abstract: While deep generative models have succeeded in image processing, natural language processing, and reinforcement learning, training that involves discrete random variables remains challenging due to the high variance of its g... |
Title: Query-Adaptive Predictive Inference with Partial Labels Abstract: The cost and scarcity of fully supervised labels in statistical machine learning encourage using partially labeled data for model validation as a cheaper and more accessible alternative. Effectively collecting and leveraging weakly supervised data... |
Title: Location-based Twitter Filtering for the Creation of Low-Resource Language Datasets in Indonesian Local Languages Abstract: Twitter contains an abundance of linguistic data from the real world. We examine Twitter for user-generated content in low-resource languages such as local Indonesian. For NLP to work in In... |
Title: A Multiple kernel testing procedure for non-proportional hazards in factorial designs Abstract: In this paper we propose a Multiple kernel testing procedure to infer survival data when several factors (e.g. different treatment groups, gender, medical history) and their interaction are of interest simultaneously.... |
Title: Test-Time Adaptation for Visual Document Understanding Abstract: Self-supervised pretraining has been able to produce transferable representations for various visual document understanding (VDU) tasks. However, the ability of such representations to adapt to new distribution shifts at test-time has not been stud... |
Title: Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking Abstract: Rankings have become the primary interface in two-sided online markets. Many have noted that the rankings not only affect the satisfaction of the users (e.g., customers, listeners, employers, travelers), but that the position in... |
Title: Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions Abstract: Stochastic gradient descent (SGD) is a pillar of modern machine learning, serving as the go-to optimization algorithm for a diverse array of problems. While the empirical success of SGD is often attribut... |
Title: TeKo: Text-Rich Graph Neural Networks with External Knowledge Abstract: Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i.e., networks). Typical GNNs and their variants follow a message-passing manner that obtains network representations by... |
Title: CLNode: Curriculum Learning for Node Classification Abstract: Node classification is a fundamental graph-based task that aims to predict the classes of unlabeled nodes, for which Graph Neural Networks (GNNs) are the state-of-the-art methods. In current GNNs, training nodes (or training samples) are treated equal... |
Title: On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation Abstract: Inspired by the concept of preconditioning, we propose a novel method to increase adaptation speed for gradient-based meta-learning methods without incurring extra parameters. We demonstrate that recasting the optimization prob... |
Title: Latency Control for Keyword Spotting Abstract: Conversational agents commonly utilize keyword spotting (KWS) to initiate voice interaction with the user. For user experience and privacy considerations, existing approaches to KWS largely focus on accuracy, which can often come at the expense of introduced latency... |
Title: Resource-Constrained Edge AI with Early Exit Prediction Abstract: By leveraging the data sample diversity, the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process. However, intermediate classifiers of the early exits introduce additiona... |
Title: CARD: Classification and Regression Diffusion Models Abstract: Learning the distribution of a continuous or categorical response variable $\boldsymbol y$ given its covariates $\boldsymbol x$ is a fundamental problem in statistics and machine learning. Deep neural network-based supervised learning algorithms have... |
Title: ALASCA: Rethinking Label Smoothing for Deep Learning Under Label Noise Abstract: As label noise, one of the most popular distribution shifts, severely degrades deep neural networks' generalization performance, robust training with noisy labels is becoming an important task in modern deep learning. In this paper,... |
Title: Global Convergence of Federated Learning for Mixed Regression Abstract: This paper studies the problem of model training under Federated Learning when clients exhibit cluster structure. We contextualize this problem in mixed regression, where each client has limited local data generated from one of $k$ unknown r... |
Title: A Survey on Gradient Inversion: Attacks, Defenses and Future Directions Abstract: Recent studies have shown that the training samples can be recovered from gradients, which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of extensive surveys covering recent advances and thorough an... |
Title: Differentiable Top-k Classification Learning Abstract: The top-k classification accuracy is one of the core metrics in machine learning. Here, k is conventionally a positive integer, such as 1 or 5, leading to top-1 or top-5 training objectives. In this work, we relax this assumption and optimize the model for m... |
Title: FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank Abstract: FOLD-R++ is a new inductive learning algorithm for binary classification tasks. It generates an (explainable) normal logic program for mixed type (numerical and categorical) data. We present a customized FOLD-R++ algori... |
Title: Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlook Abstract: Knowledge management systems are in high demand for industrial researchers, chemical or research enterprises, or evidence-based decision making. However, existing systems have limitations in categorizing and organizin... |
Title: Diffusion Transport Alignment Abstract: The integration of multimodal data presents a challenge in cases when the study of a given phenomena by different instruments or conditions generates distinct but related domains. Many existing data integration methods assume a known one-to-one correspondence between domai... |
Title: VCT: A Video Compression Transformer Abstract: We show how transformers can be used to vastly simplify neural video compression. Previous methods have been relying on an increasing number of architectural biases and priors, including motion prediction and warping operations, resulting in complex models. Instead,... |
Title: Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models Abstract: Diffusion probabilistic models (DPMs) are a class of powerful deep generative models (DGMs). Despite their success, the iterative generation process over the full timesteps is much less efficient than other DGMs suc... |
Title: Can pruning improve certified robustness of neural networks? Abstract: With the rapid development of deep learning, the sizes of neural networks become larger and larger so that the training and inference often overwhelm the hardware resources. Given the fact that neural networks are often over-parameterized, on... |
Title: Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack Abstract: The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available. However, the high computational cost (e.g., 100 times more than that of the... |
Title: Online Contextual Decision-Making with a Smart Predict-then-Optimize Method Abstract: We study an online contextual decision-making problem with resource constraints. At each time period, the decision-maker first predicts a reward vector and resource consumption matrix based on a given context vector and then so... |
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