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Title: Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power Abstract: It is well-known that modern neural networks are vulnerable to adversarial examples. To mitigate this problem, a series of robust learning algorithms have been proposed. However, although the robust training error ... |
Title: On the Convergence of Semi-Relaxed Sinkhorn with Marginal Constraint and OT Distance Gaps Abstract: This paper presents consideration of the Semi-Relaxed Sinkhorn (SR-Sinkhorn) algorithm for the semi-relaxed optimal transport (SROT) problem, which relaxes one marginal constraint of the standard OT problem. For e... |
Title: Raising the Bar in Graph-level Anomaly Detection Abstract: Graph-level anomaly detection has become a critical topic in diverse areas, such as financial fraud detection and detecting anomalous activities in social networks. While most research has focused on anomaly detection for visual data such as images, wher... |
Title: Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers Abstract: Random Forests (RFs) are widely used Machine Learning models in low-power embedded devices, due to their hardware friendly operation and high accuracy on practically relevant tasks. The accuracy of a RF often increases with the ... |
Title: Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control Abstract: Multi-agent reinforcement learning (MARL) has been applied and shown great potential in multi-intersections traffic signal control, where multiple agents, one for each intersection, must cooperate toget... |
Title: Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements Abstract: Objective. This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circum... |
Title: Improving Bidding and Playing Strategies in the Trick-Taking game Wizard using Deep Q-Networks Abstract: In this work, the trick-taking game Wizard with a separate bidding and playing phase is modeled by two interleaved partially observable Markov decision processes (POMDP). Deep Q-Networks (DQN) are used to emp... |
Title: Counterfactual Analysis in Dynamic Models: Copulas and Bounds Abstract: We provide an explicit model of the causal mechanism in a structural causal model (SCM) with the goal of estimating counterfactual quantities of interest (CQIs). We propose some standard dependence structures, i.e. copulas, as base cases for... |
Title: Error Bound of Empirical $\ell_2$ Risk Minimization for Noisy Standard and Generalized Phase Retrieval Problems Abstract: A noisy generalized phase retrieval (NGPR) problem refers to a problem of estimating $x_0 \in \mathbb{C}^d$ by noisy quadratic samples $\big\{x_0^*A_kx_0+\eta_k\big\}_{k=1}^n$ where $A_k$ is ... |
Title: Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows Abstract: Electricity is traded on various markets with different time horizons and regulations. Short-term trading becomes increasingly important due to higher penetration of renewables. In Germany, the intraday electr... |
Title: Isolating and Leveraging Controllable and Noncontrollable Visual Dynamics in World Models Abstract: World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios such as autonomous driving, there commonly exists noncontrollable dynamics independent of the act... |
Title: Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks Abstract: Visual object recognition has been extensively studied in both neuroscience and computer vision. Recently, the most popular class of artificial systems for this task, deep convolutional neural ... |
Title: Global Convergence of Over-parameterized Deep Equilibrium Models Abstract: A deep equilibrium model (DEQ) is implicitly defined through an equilibrium point of an infinite-depth weight-tied model with an input-injection. Instead of infinite computations, it solves an equilibrium point directly with root-finding ... |
Title: A Design Space for Explainable Ranking and Ranking Models Abstract: Item ranking systems support users in multi-criteria decision-making tasks. Users need to trust rankings and ranking algorithms to reflect user preferences nicely while avoiding systematic errors and biases. However, today only few approaches he... |
Title: fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems Abstract: Recently, Deep Neural Networks (DNNs) have achieved remarkable performances in many applications, while several studies have enhanced their vulnerabilities to malicious attack... |
Title: X-ViT: High Performance Linear Vision Transformer without Softmax Abstract: Vision transformers have become one of the most important models for computer vision tasks. Although they outperform prior works, they require heavy computational resources on a scale that is quadratic to the number of tokens, $N$. This ... |
Title: End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control Abstract: It is well-known that inverse dynamics models can improve tracking performance in robot control. These models need to precisely capture the robot dynamics, which consist of well-understood components, e.g.... |
Title: Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions Abstract: A significant gap remains between today's visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts. We introduce Bongar... |
Title: Painful intelligence: What AI can tell us about human suffering Abstract: This book uses the modern theory of artificial intelligence (AI) to understand human suffering or mental pain. Both humans and sophisticated AI agents process information about the world in order to achieve goals and obtain rewards, which ... |
Title: Generalization Bounds for Gradient Methods via Discrete and Continuous Prior Abstract: Proving algorithm-dependent generalization error bounds for gradient-type optimization methods has attracted significant attention recently in learning theory. However, most existing trajectory-based analyses require either re... |
Title: AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation Abstract: In an asynchronous federated learning framework, the server updates the global model once it receives an update from a client instead of waiting for all the updates to arrive as in the synchronous sett... |
Title: A reconfigurable integrated electronic tongue and its use in accelerated analysis of juices and wines Abstract: Potentiometric electronic tongues (ETs) leveraging trends in miniaturization and internet of things (IoT) bear promise for facile mobile chemical analysis of complex multicomponent liquids, such as bev... |
Title: A Sea of Words: An In-Depth Analysis of Anchors for Text Data Abstract: Anchors [Ribeiro et al. (2018)] is a post-hoc, rule-based interpretability method. For text data, it proposes to explain a decision by highlighting a small set of words (an anchor) such that the model to explain has similar outputs when they... |
Title: Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier Abstract: Nowadays, a tremendous amount of human communications take place on the Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, ... |
Title: Block-coordinate Frank-Wolfe algorithm and convergence analysis for semi-relaxed optimal transport problem Abstract: The optimal transport (OT) problem has been used widely for machine learning. It is necessary for computation of an OT problem to solve linear programming with tight mass-conservation constraints.... |
Title: Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval Abstract: The ability to accurately model the fitness landscape of protein sequences is critical to a wide range of applications, from quantifying the effects of human variants on disease likelihood, to predicti... |
Title: CIGMO: Categorical invariant representations in a deep generative framework Abstract: Data of general object images have two most common structures: (1) each object of a given shape can be rendered in multiple different views, and (2) shapes of objects can be categorized in such a way that the diversity of shape... |
Title: Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions Abstract: One of the grand challenges of utilizing machine learning for the discovery of innovative new polymers lies in the difficulty of accurately representing the complex structures of polymeric materi... |
Title: HOUDINI: Escaping from Moderately Constrained Saddles Abstract: We give the first polynomial time algorithms for escaping from high-dimensional saddle points under a moderate number of constraints. Given gradient access to a smooth function $f \colon \mathbb R^d \to \mathbb R$ we show that (noisy) gradient desce... |
Title: Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture Abstract: Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation. In forward modeling problems, PINNs are meshless ... |
Title: Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games Abstract: We study the problem of finding the Nash equilibrium in a two-player zero-sum Markov game. Due to its formulation as a minimax optimization program, a natural approach to solve the problem is to perform gradient descent/ascent wit... |
Title: Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences Abstract: Counterfactual interventions are a powerful tool to explain the decisions of a black-box decision process, and to enable algorithmic recourse. They are a sequence of actions that, if performed by... |
Title: Group GAN Abstract: Generating multivariate time series is a promising approach for sharing sensitive data in many medical, financial, and IoT applications. A common type of multivariate time series originates from a single source such as the biometric measurements from a medical patient. This leads to complex d... |
Title: Subverting machines, fluctuating identities: Re-learning human categorization Abstract: Most machine learning systems that interact with humans construct some notion of a person's "identity," yet the default paradigm in AI research envisions identity with essential attributes that are discrete and static. In sta... |
Title: On Consistency in Graph Neural Network Interpretation Abstract: Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies up... |
Title: Can Foundation Models Help Us Achieve Perfect Secrecy? Abstract: A key promise of machine learning is the ability to assist users with personal tasks. Because the personal context required to make accurate predictions is often sensitive, we require systems that protect privacy. A gold standard privacy-preserving... |
Title: Effective Abstract Reasoning with Dual-Contrast Network Abstract: As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven's Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence. Unlike previous methods tha... |
Title: Off-Beat Multi-Agent Reinforcement Learning Abstract: We investigate model-free multi-agent reinforcement learning (MARL) in environments where off-beat actions are prevalent, i.e., all actions have pre-set execution durations. During execution durations, the environment changes are influenced by, but not synchr... |
Title: Hazard Gradient Penalty for Survival Analysis Abstract: Survival analysis appears in various fields such as medicine, economics, engineering, and business. Recent studies showed that the Ordinary Differential Equation (ODE) modeling framework unifies many existing survival models while the framework is flexible ... |
Title: Incorporating the Barzilai-Borwein Adaptive Step Size into Sugradient Methods for Deep Network Training Abstract: In this paper, we incorporate the Barzilai-Borwein step size into gradient descent methods used to train deep networks. This allows us to adapt the learning rate using a two-point approximation to th... |
Title: Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss Abstract: A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. Stochastic Gradient Langevin Dynamics... |
Title: DP-PCA: Statistically Optimal and Differentially Private PCA Abstract: We study the canonical statistical task of computing the principal component from $n$ i.i.d.~data in $d$ dimensions under $(\varepsilon,\delta)$-differential privacy. Although extensively studied in literature, existing solutions fall short o... |
Title: Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters Abstract: Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of $Q$-functions can be leveraged as the primary source of p... |
Title: ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting Abstract: Graph Neural Networks (GNNs) have achieved enormous success in tackling analytical problems on graph data. Most GNNs interpret nearly all the node connections as inductive bias with feature smoothness, and implicitly assume... |
Title: Maximum Likelihood Training of Implicit Nonlinear Diffusion Models Abstract: Whereas diverse variations of diffusion models exist, expanding the linear diffusion into a nonlinear diffusion process is investigated only by a few works. The nonlinearity effect has been hardly understood, but intuitively, there woul... |
Title: FedFormer: Contextual Federation with Attention in Reinforcement Learning Abstract: A core issue in federated reinforcement learning is defining how to aggregate insights from multiple agents into one. This is commonly done by taking the average of each participating agent's model weights into one common model (... |
Title: FedAvg with Fine Tuning: Local Updates Lead to Representation Learning Abstract: The Federated Averaging (FedAvg) algorithm, which consists of alternating between a few local stochastic gradient updates at client nodes, followed by a model averaging update at the server, is perhaps the most commonly used method ... |
Title: Safety Aware Changepoint Detection for Piecewise i.i.d. Bandits Abstract: In this paper, we consider the setting of piecewise i.i.d. bandits under a safety constraint. In this piecewise i.i.d. setting, there exists a finite number of changepoints where the mean of some or all arms change simultaneously. We intro... |
Title: Asymptotic Convergence Rate and Statistical Inference for Stochastic Sequential Quadratic Programming Abstract: We apply a stochastic sequential quadratic programming (StoSQP) algorithm to solve constrained nonlinear optimization problems, where the objective is stochastic and the constraints are deterministic. ... |
Title: Learning with Stochastic Orders Abstract: Learning high-dimensional distributions is often done with explicit likelihood modeling or implicit modeling via minimizing integral probability metrics (IPMs). In this paper, we expand this learning paradigm to stochastic orders, namely, the convex or Choquet order betw... |
Title: Membership Inference Attack Using Self Influence Functions Abstract: Member inference (MI) attacks aim to determine if a specific data sample was used to train a machine learning model. Thus, MI is a major privacy threat to models trained on private sensitive data, such as medical records. In MI attacks one may ... |
Title: SeedGNN: Graph Neural Networks for Supervised Seeded Graph Matching Abstract: Recently, there have been significant interests in designing Graph Neural Networks (GNNs) for seeded graph matching, which aims to match two (unlabeled) graphs using only topological information and a small set of seeds. However, most ... |
Title: Fast variable selection makes scalable Gaussian process BSS-ANOVA a speedy and accurate choice for tabular and time series regression Abstract: Gaussian processes (GPs) are non-parametric regression engines with a long history. They are often overlooked in modern machine learning contexts because of scalability ... |
Title: Reinforcement Learning Approach for Mapping Applications to Dataflow-Based Coarse-Grained Reconfigurable Array Abstract: The Streaming Engine (SE) is a Coarse-Grained Reconfigurable Array which provides programming flexibility and high-performance with energy efficiency. An application program to be executed on ... |
Title: Global Normalization for Streaming Speech Recognition in a Modular Framework Abstract: We introduce the Globally Normalized Autoregressive Transducer (GNAT) for addressing the label bias problem in streaming speech recognition. Our solution admits a tractable exact computation of the denominator for the sequence... |
Title: Transformer for Partial Differential Equations' Operator Learning Abstract: Data-driven learning of partial differential equations' solution operators has recently emerged as a promising paradigm for approximating the underlying solutions. The solution operators are usually parameterized by deep learning models ... |
Title: Explaining Preferences with Shapley Values Abstract: While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propose \textsc{Pref-SHAP}, a Shapley value-based model explanation framework for pair... |
Title: Contextual Adapters for Personalized Speech Recognition in Neural Transducers Abstract: Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data. A standard way to address this issue is with shallow fusion methods at inference time... |
Title: Mixed Federated Learning: Joint Decentralized and Centralized Learning Abstract: Federated learning (FL) enables learning from decentralized privacy-sensitive data, with computations on raw data confined to take place at edge clients. This paper introduces mixed FL, which incorporates an additional loss term cal... |
Title: A Unified Analysis of Federated Learning with Arbitrary Client Participation Abstract: Federated learning (FL) faces challenges of intermittent client availability and computation/communication efficiency. As a result, only a small subset of clients can participate in FL at a given time. It is important to under... |
Title: Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures Abstract: This paper considers the Pointer Value Retrieval (PVR) benchmark introduced in [ZRKB21], where a 'reasoning' function acts on a string of digits to produce the label. More generally, the paper considers the learni... |
Title: A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions Abstract: Multiple chronic conditions (MCC) are one of the biggest challenges of modern times. The evolution of MCC follows a complex stochastic process that is influenced by a variety of ris... |
Title: Quark: Controllable Text Generation with Reinforced Unlearning Abstract: Large-scale language models often learn behaviors that are misaligned with user expectations. Generated text may contain offensive or toxic language, contain significant repetition, or be of a different sentiment than desired by the user. W... |
Title: RIGID: Robust Linear Regression with Missing Data Abstract: We present a robust framework to perform linear regression with missing entries in the features. By considering an elliptical data distribution, and specifically a multivariate normal model, we are able to conditionally formulate a distribution for the ... |
Title: BagFlip: A Certified Defense against Data Poisoning Abstract: Machine learning models are vulnerable to data-poisoning attacks, in which an attacker maliciously modifies the training set to change the prediction of a learned model. In a trigger-less attack, the attacker can modify the training set but not the te... |
Title: Flexible Group Fairness Metrics for Survival Analysis Abstract: Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorithmic fairness in regression and classification however there has been... |
Title: Faster Optimization on Sparse Graphs via Neural Reparametrization Abstract: In mathematical optimization, second-order Newton's methods generally converge faster than first-order methods, but they require the inverse of the Hessian, hence are computationally expensive. However, we discover that on sparse graphs,... |
Title: A Hybrid Neural Autoencoder for Sensory Neuroprostheses and Its Applications in Bionic Vision Abstract: Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capacities. However, sensations elicited by current devices often appear artificial and distorte... |
Title: Differentially Private Decoding in Large Language Models Abstract: Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart. In practice, the pre-trained model is adapted to a wide array of tasks via fine-tuning on tas... |
Title: Fairness in Recommendation: A Survey Abstract: As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommen... |
Title: Denial-of-Service Attack on Object Detection Model Using Universal Adversarial Perturbation Abstract: Adversarial attacks against deep learning-based object detectors have been studied extensively in the past few years. The proposed attacks aimed solely at compromising the models' integrity (i.e., trustworthines... |
Title: Approximate Q-learning and SARSA(0) under the $ε$-greedy Policy: a Differential Inclusion Analysis Abstract: Q-learning and SARSA(0) with linear function approximation, under $\epsilon$-greedy exploration, are leading methods to estimate the optimal policy in Reinforcement Learning (RL). It has been empirically ... |
Title: Fight Poison with Poison: Detecting Backdoor Poison Samples via Decoupling Benign Correlations Abstract: In this work, we study poison samples detection for defending against backdoor poisoning attacks on deep neural networks (DNNs). A principled idea underlying prior arts on this problem is to utilize the backd... |
Title: Emergent organization of receptive fields in networks of excitatory and inhibitory neurons Abstract: Local patterns of excitation and inhibition that can generate neural waves are studied as a computational mechanism underlying the organization of neuronal tunings. Sparse coding algorithms based on networks of e... |
Title: Circumventing Backdoor Defenses That Are Based on Latent Separability Abstract: Deep learning models are vulnerable to backdoor poisoning attacks. In particular, adversaries can embed hidden backdoors into a model by only modifying a very small portion of its training data. On the other hand, it has also been co... |
Title: Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets Abstract: Detailed mobile sensing data from phones, watches, and fitness trackers offer an unparalleled opportunity to quantify and act upon previously unmeasurable behavioral changes in order t... |
Title: Tensor Program Optimization with Probabilistic Programs Abstract: Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search space and effective search. Most existing efforts adopt a search space... |
Title: Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods Abstract: Addressing the challenge of scaling-up epidemiological inference to complex and heterogeneous models, we introduce Poisson Approximate Likelihood (PAL) methods. In contrast to the popular ODE approa... |
Title: MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control Abstract: Embodied agents in continuous control domains have had limited exposure to tasks allowing to explore musculoskeletal properties that enable agile and nimble behaviors in biological beings. The sophistication behind neuro-musc... |
Title: VectorAdam for Rotation Equivariant Geometry Optimization Abstract: The rise of geometric problems in machine learning has necessitated the development of equivariant methods, which preserve their output under the action of rotation or some other transformation. At the same time, the Adam optimization algorithm ... |
Title: DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning Abstract: Predicted inter-chain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number of methods have been developed to reconstruct protei... |
Title: Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes Abstract: We study offline reinforcement learning (RL) in partially observable Markov decision processes. In particular, we aim to learn an optimal policy from a dataset colle... |
Title: Evolution of beliefs in social networks Abstract: Evolution of beliefs of a society are a product of interactions between people (horizontal transmission) in the society over generations (vertical transmission). Researchers have studied both horizontal and vertical transmission separately. Extending prior work, ... |
Title: Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training Abstract: Feedback-driven recurrent spiking neural networks (RSNNs) are powerful computational models that can mimic dynamical systems. However, the presence of a feedback loop from the readout to the recurrent layer de-stabi... |
Title: Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement Learning Abstract: A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective. Given the ubiquity of networked systems, such work has broad practical applications in a variety of sit... |
Title: Understanding new tasks through the lens of training data via exponential tilting Abstract: Deploying machine learning models to new tasks is a major challenge despite the large size of the modern training datasets. However, it is conceivable that the training data can be reweighted to be more representative of ... |
Title: Predictor-corrector algorithms for stochastic optimization under gradual distribution shift Abstract: Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Although most problems are solved in discrete t... |
Title: Pruning has a disparate impact on model accuracy Abstract: Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that pruning may create or exacerbate disparate impacts. The paper sheds light on the... |
Title: Efficient Approximation of Gromov-Wasserstein Distance using Importance Sparsification Abstract: As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for the matching problems of structured data like point clouds and graphs. However, its application in practice is ... |
Title: Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations Abstract: Neural networks have achieved tremendous success in a large variety of applications. However, their memory footprint and computational demand can render them impractical in application settings with l... |
Title: Learning Dialogue Representations from Consecutive Utterances Abstract: Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data scarcity. In this paper, we introduce Dialogue Sentence Embeddi... |
Title: Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment Abstract: Multi-armed bandit (MAB) is a classic model for understanding the exploration-exploitation trade-off. The traditional MAB model for recommendation systems assumes the user stays in the system for the entire learning horiz... |
Title: Unequal Covariance Awareness for Fisher Discriminant Analysis and Its Variants in Classification Abstract: Fisher Discriminant Analysis (FDA) is one of the essential tools for feature extraction and classification. In addition, it motivates the development of many improved techniques based on the FDA to adapt to... |
Title: Training and Inference on Any-Order Autoregressive Models the Right Way Abstract: Conditional inference on arbitrary subsets of variables is a core problem in probabilistic inference with important applications such as masked language modeling and image inpainting. In recent years, the family of Any-Order Autore... |
Title: Revealing the Dark Secrets of Masked Image Modeling Abstract: Masked image modeling (MIM) as pre-training is shown to be effective for numerous vision downstream tasks, but how and where MIM works remain unclear. In this paper, we compare MIM with the long-dominant supervised pre-trained models from two perspect... |
Title: Mitigating barren plateaus of variational quantum eigensolvers Abstract: Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent works have pointed out that the performance of VQAs greatly relies on the capability of the ansatzes and i... |
Title: Verifying Learning-Based Robotic Navigation Systems Abstract: Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for various tasks in which complex policies are learned within reactive systems. In parallel, there has recently been significant research on verifying deep neural networks... |
Title: Selective Classification Via Neural Network Training Dynamics Abstract: Selective classification is the task of rejecting inputs a model would predict incorrectly on through a trade-off between input space coverage and model accuracy. Current methods for selective classification impose constraints on either the ... |
Title: Training ReLU networks to high uniform accuracy is intractable Abstract: Statistical learning theory provides bounds on the necessary number of training samples needed to reach a prescribed accuracy in a learning problem formulated over a given target class. This accuracy is typically measured in terms of a gene... |
Title: Semantic Parsing of Interpage Relations Abstract: Page-level analysis of documents has been a topic of interest in digitization efforts, and multimodal approaches have been applied to both classification and page stream segmentation. In this work, we focus on capturing finer semantic relations between pages of a... |
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