text stringlengths 0 4.09k |
|---|
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models Abstract: State-of-the-art causal discovery methods usually assume that the observational data is complete. However, the missing data problem is pervasive in many practical scenarios such as clinical trials, economics... |
Title: Probabilistic Systems with Hidden State and Unobservable Transitions Abstract: We consider probabilistic systems with hidden state and unobservable transitions, an extension of Hidden Markov Models (HMMs) that in particular admits unobservable {\epsilon}-transitions (also called null transitions), allowing state... |
Title: Comparison of Deep Learning Segmentation and Multigrader-annotated Mandibular Canals of Multicenter CBCT scans Abstract: Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarc... |
Title: MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task Abstract: We present a machine sound dataset to benchmark domain generalization techniques for anomalous sound detection (ASD). To handle performance degradation caused by domain shifts that ... |
Title: TraClets: Harnessing the power of computer vision for trajectory classification Abstract: Due to the advent of new mobile devices and tracking sensors in recent years, huge amounts of data are being produced every day. Therefore, novel methodologies need to emerge that dive through this vast sea of information a... |
Title: Automated Dynamic Algorithm Configuration Abstract: The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there ... |
Title: Transformers from an Optimization Perspective Abstract: Deep learning models such as the Transformer are often constructed by heuristics and experience. To provide a complementary foundation, in this work we study the following problem: Is it possible to find an energy function underlying the Transformer model, ... |
Title: EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks Abstract: Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGN... |
Title: How Tempering Fixes Data Augmentation in Bayesian Neural Networks Abstract: While Bayesian neural networks (BNNs) provide a sound and principled alternative to standard neural networks, an artificial sharpening of the posterior usually needs to be applied to reach comparable performance. This is in stark contras... |
Title: Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation Abstract: In general, to draw robust conclusions from a dataset, all the analyzed population must be represented on said dataset. Having a dataset that does not fulfill this condition normally leads to selection bias. Additionally, g... |
Title: Sample-Efficient Optimisation with Probabilistic Transformer Surrogates Abstract: Faced with problems of increasing complexity, recent research in Bayesian Optimisation (BO) has focused on adapting deep probabilistic models as flexible alternatives to Gaussian Processes (GPs). In a similar vein, this paper inves... |
Title: (De-)Randomized Smoothing for Decision Stump Ensembles Abstract: Tree-based models are used in many high-stakes application domains such as finance and medicine, where robustness and interpretability are of utmost importance. Yet, methods for improving and certifying their robustness are severely under-explored,... |
Title: Dynamic Domain Generalization Abstract: Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanis... |
Title: Fast Causal Orientation Learning in Directed Acyclic Graphs Abstract: Causal relationships among a set of variables are commonly represented by a directed acyclic graph. The orientations of some edges in the causal DAG can be discovered from observational/interventional data. Further edges can be oriented by ite... |
Title: Federated Semi-Supervised Learning with Prototypical Networks Abstract: With the increasing computing power of edge devices, Federated Learning (FL) emerges to enable model training without privacy concerns. The majority of existing studies assume the data are fully labeled on the client side. In practice, howev... |
Title: Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits Abstract: We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts. We adapt the information-theoretic perspect... |
Title: Client Selection in Nonconvex Federated Learning: Improved Convergence Analysis for Optimal Unbiased Sampling Strategy Abstract: Federated learning (FL) is a distributed machine learning paradigm that selects a subset of clients to participate in training to reduce communication burdens. However, partial client ... |
Title: Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design Abstract: Our world is ambiguous and this is reflected in the data we use to train our algorithms. This is especially true when we try to model natural processes where collected data is affected by noisy measur... |
Title: Fairness and Welfare Quantification for Regret in Multi-Armed Bandits Abstract: We extend the notion of regret with a welfarist perspective. Focussing on the classic multi-armed bandit (MAB) framework, the current work quantifies the performance of bandit algorithms by applying a fundamental welfare function, na... |
Title: Standalone Neural ODEs with Sensitivity Analysis Abstract: This paper presents the Standalone Neural ODE (sNODE), a continuous-depth neural ODE model capable of describing a full deep neural network. This uses a novel nonlinear conjugate gradient (NCG) descent optimization scheme for training, where the Sobolev ... |
Title: Combining observational datasets from multiple environments to detect hidden confounding Abstract: A common assumption in causal inference from observational data is the assumption of no hidden confounding. Yet it is, in general, impossible to verify the presence of hidden confounding factors from a single datas... |
Title: Auditing Differential Privacy in High Dimensions with the Kernel Quantum R\'enyi Divergence Abstract: Differential privacy (DP) is the de facto standard for private data release and private machine learning. Auditing black-box DP algorithms and mechanisms to certify whether they satisfy a certain DP guarantee is... |
Title: Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation Abstract: This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network where the interactions of the agents duri... |
Title: Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer Abstract: Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, wh... |
Title: Non-Markovian policies occupancy measures Abstract: A central object of study in Reinforcement Learning (RL) is the Markovian policy, in which an agent's actions are chosen from a memoryless probability distribution, conditioned only on its current state. The family of Markovian policies is broad enough to be in... |
Title: Guided Exploration of Data Summaries Abstract: Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. A useful summary contains k individually uniform sets that... |
Title: Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications Abstract: This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs. To this end, we r... |
Title: Counterfactual Fairness with Partially Known Causal Graph Abstract: Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair machine learning that are built on causal inference ascertain discrim... |
Title: Deep Ensembles for Graphs with Higher-order Dependencies Abstract: Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximation of the true neighborhood structure. In the presence of higher-... |
Title: Prototype Based Classification from Hierarchy to Fairness Abstract: Artificial neural nets can represent and classify many types of data but are often tailored to particular applications -- e.g., for "fair" or "hierarchical" classification. Once an architecture has been selected, it is often difficult for humans... |
Title: RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation Abstract: Recipe recommendation systems play an essential role in helping people decide what to eat. Existing recipe recommendation systems typically focused on content-based or collaborative filtering approaches, ignoring the higher-order... |
Title: What Dense Graph Do You Need for Self-Attention? Abstract: Transformers have made progress in miscellaneous tasks, but suffer from quadratic computational and memory complexities. Recent works propose sparse Transformers with attention on sparse graphs to reduce complexity and remain strong performance. While ef... |
Title: Inference and Sampling for Archimax Copulas Abstract: Understanding multivariate dependencies in both the bulk and the tails of a distribution is an important problem for many applications, such as ensuring algorithms are robust to observations that are infrequent but have devastating effects. Archimax copulas a... |
Title: Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces Abstract: We study a class of dynamical systems modelled as Markov chains that admit an invariant distribution via the corresponding transfer, or Koopman, operator. While data-driven algorithms to reconstruct such ope... |
Title: Learning to Control Linear Systems can be Hard Abstract: In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regret of the online learning of the Linear Quadratic Regulator (LQR). Prior resu... |
Title: Group-invariant max filtering Abstract: Given a real inner product space $V$ and a group $G$ of linear isometries, we construct a family of $G$-invariant real-valued functions on $V$ that we call max filters. In the case where $V=\mathbb{R}^d$ and $G$ is finite, a suitable max filter bank separates orbits, and i... |
Title: Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities Abstract: The growing deployment efforts of 5G networks globally has led to the acceleration of the businesses/services' digital transformation. This growth has led to the need for new communication technologies that wil... |
Title: Double Deep Q Networks for Sensor Management in Space Situational Awareness Abstract: We present a novel Double Deep Q Network (DDQN) application to a sensor management problem in space situational awareness (SSA). Frequent launches of satellites into Earth orbit pose a significant sensor management challenge, w... |
Title: Average Adjusted Association: Efficient Estimation with High Dimensional Confounders Abstract: The log odds ratio is a common parameter to measure association between (binary) outcome and exposure variables. Much attention has been paid to its parametric but robust estimation, or its nonparametric estimation as ... |
Title: Contrastive Siamese Network for Semi-supervised Speech Recognition Abstract: This paper introduces contrastive siamese (c-siam) network, an architecture for leveraging unlabeled acoustic data in speech recognition. c-siam is the first network that extracts high-level linguistic information from speech by matchin... |
Title: Benign Overparameterization in Membership Inference with Early Stopping Abstract: Does a neural network's privacy have to be at odds with its accuracy? In this work, we study the effects the number of training epochs and parameters have on a neural network's vulnerability to membership inference (MI) attacks, wh... |
Title: Dual Convexified Convolutional Neural Networks Abstract: We propose the framework of dual convexified convolutional neural networks (DCCNNs). In this framework, we first introduce a primal learning problem motivated from convexified convolutional neural networks (CCNNs), and then construct the dual convex traini... |
Title: Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos Abstract: Unsupervised object-centric learning aims to represent the modular, compositional, and causal structure of a scene as a set of object representations and thereby promises to resolve many critical limitations of traditional ... |
Title: Finite mixture of skewed sub-Gaussian stable distributions Abstract: We propose the finite mixture of skewed sub-Gaussian stable distributions. The maximum likelihood estimator for the parameters of proposed finite mixture model is computed through the expectation-maximization algorithm. The proposed model conta... |
Title: Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification Abstract: Compressive spectral imaging (CSI) has emerged as an attractive compression and sensing technique, primarily to sense spectral regions where traditional systems result in highly costly such as in the near-infrared spectru... |
Title: AANG: Automating Auxiliary Learning Abstract: When faced with data-starved or highly complex end-tasks, it is commonplace for machine learning practitioners to introduce auxiliary objectives as supplementary learning signals. Whilst much work has been done to formulate useful auxiliary objectives, their construc... |
Title: Sharpness-Aware Training for Free Abstract: Modern deep neural networks (DNNs) have achieved state-of-the-art performances but are typically over-parameterized. The over-parameterization may result in undesirably large generalization error in the absence of other customized training strategies. Recently, a line ... |
Title: Surrogate modeling for Bayesian optimization beyond a single Gaussian process Abstract: Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions emerge in applications as diverse as hyperparameter tuning, drug discovery, and roboti... |
Title: Capturing Graphs with Hypo-Elliptic Diffusions Abstract: Convolutional layers within graph neural networks operate by aggregating information about local neighbourhood structures; one common way to encode such substructures is through random walks. The distribution of these random walks evolves according to a di... |
Title: Solving infinite-horizon POMDPs with memoryless stochastic policies in state-action space Abstract: Reward optimization in fully observable Markov decision processes is equivalent to a linear program over the polytope of state-action frequencies. Taking a similar perspective in the case of partially observable M... |
Title: Generalizing Brain Decoding Across Subjects with Deep Learning Abstract: Decoding experimental variables from brain imaging data is gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over ... |
Title: Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering Abstract: We propose a novel approach to the problem of clustering hierarchically aggregated time-series data, which has remained an understudied problem though it has several commercial applications. We first group time seri... |
Title: Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration Abstract: From logistics to the natural sciences, combinatorial optimisation on graphs underpins numerous real-world applications. Reinforcement learning (RL) has shown particular promise in this setting as it can adapt to spec... |
Title: Spartan: Differentiable Sparsity via Regularized Transportation Abstract: We present Spartan, a method for training sparse neural network models with a predetermined level of sparsity. Spartan is based on a combination of two techniques: (1) soft top-k masking of low-magnitude parameters via a regularized optima... |
Title: Scalable Interpretability via Polynomials Abstract: Generalized Additive Models (GAMs) have quickly become the leading choice for fully-interpretable machine learning. However, unlike uninterpretable methods such as DNNs, they lack expressive power and easy scalability, and are hence not a feasible alternative f... |
Title: Bayesian Robust Graph Contrastive Learning Abstract: Graph Neural Networks (GNNs) have been widely used to learn node representations and with outstanding performance on various tasks such as node classification. However, noise, which inevitably exists in real-world graph data, would considerably degrade the per... |
Title: Robust Counterfactual Explanations for Random Forests Abstract: Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier. Several heuristic and optimal methods have been proposed to generate these explanations. However, the robustness of counterfact... |
Title: Neural Basis Models for Interpretability Abstract: Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfuln... |
Title: Meta-Learning Adversarial Bandits Abstract: We study online learning with bandit feedback across multiple tasks, with the goal of improving average performance across tasks if they are similar according to some natural task-similarity measure. As the first to target the adversarial setting, we design a unified m... |
Title: FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness Abstract: Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to address this problem by trading... |
Title: PSL is Dead. Long Live PSL Abstract: Property Specification Language (PSL) is a form of temporal logic that has been mainly used in discrete domains (e.g. formal hardware verification). In this paper, we show that by merging machine learning techniques with PSL monitors, we can extend PSL to work on continuous d... |
Title: Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation Abstract: Masked image modeling (MIM) learns representations with remarkably good fine-tuning performances, overshadowing previous prevalent pre-training approaches such as image classification, instance contrastive learnin... |
Title: FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging Abstract: To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed. The network was trained to predict the optical flow between two PET frame... |
Title: Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport Abstract: The problem of optimization on Stiefel manifold, i.e., minimizing functions of (not necessarily square) matrices that satisfy orthogonality constraints, has been extensively studied, partly due to rich... |
Title: Private and Byzantine-Proof Cooperative Decision-Making Abstract: The cooperative bandit problem is a multi-agent decision problem involving a group of agents that interact simultaneously with a multi-armed bandit, while communicating over a network with delays. The central idea in this problem is to design algo... |
Title: Multiscale Voxel Based Decoding For Enhanced Natural Image Reconstruction From Brain Activity Abstract: Reconstructing perceived images from human brain activity monitored by functional magnetic resonance imaging (fMRI) is hard, especially for natural images. Existing methods often result in blurry and unintelli... |
Title: Optimizing Objective Functions from Trained ReLU Neural Networks via Sampling Abstract: This paper introduces scalable, sampling-based algorithms that optimize trained neural networks with ReLU activations. We first propose an iterative algorithm that takes advantage of the piecewise linear structure of ReLU neu... |
Title: Constrained Langevin Algorithms with L-mixing External Random Variables Abstract: Langevin algorithms are gradient descent methods augmented with additive noise, and are widely used in Markov Chain Monte Carlo (MCMC) sampling, optimization, and learning. In recent years, the non-asymptotic analysis of Langevin a... |
Title: FadMan: Federated Anomaly Detection across Multiple Attributed Networks Abstract: Anomaly subgraph detection has been widely used in various applications, ranging from cyber attack in computer networks to malicious activities in social networks. Despite an increasing need for federated anomaly detection across m... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.