Search Results for author:
Found 48 papers, 8 papers with code
Online Learning with Gaussian Payoffs and Side Observations
For the first time in the literature, we provide non-asymptotic problem-dependent lower bounds on the regret of any algorithm, which recover existing asymptotic problem-dependent lower bounds and finite-time minimax lower bounds available in the literature.
Conservative Bandits
We consider both the stochastic and the adversarial settings, where we propose, natural, yet novel strategies and analyze the price for maintaining the constraints.
Planar Object Tracking in the Wild: A Benchmark
In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment.
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent
A major challenge in understanding the generalization of deep learning is to explain why (stochastic) gradient descent can exploit the network architecture to find solutions that have good generalization performance when using high capacity models.
Generating Synthetic X-ray Images of a Person from the Surface Geometry
Furthermore, the synthetic X-ray image is parametrized and can be manipulated by adjusting a set of body markers which are also generated during the X-ray image prediction.
Privacy-Protective-GAN for Face De-identification
In this paper, we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input.
Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings
In this paper, we present a new large-scale benchmark dataset termed Aerial Imagery for Roof Segmentation (AIRS).
The Laplacian in RL: Learning Representations with Efficient Approximations
In this paper, we present a fully general and scalable method for approximating the eigenvectors of the Laplacian in a model-free RL context.
Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging
Tracking organ motion is important in image-guided interventions, but motion annotations are not always easily available.
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution.
Stochastic Learning of Additive Second-Order Penalties with Applications to Fairness
In non-convex settings, the resulting problem may be difficult to solve as the Lagrangian is not guaranteed to have a deterministic saddle-point equilibrium.
Enhanced generative adversarial network for 3D brain MRI super-resolution
Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing and analysis of available image data.
Behavior Regularized Offline Reinforcement Learning
In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment.
Game Design for Eliciting Distinguishable Behavior
The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems.
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate.
Model-based Reinforcement Learning
reinforcement-learning
+1
A Unified View of Label Shift Estimation
Our contributions include (i) consistency conditions for MLLS, which include calibration of the classifier and a confusion matrix invertibility condition that BBSE also requires; (ii) a unified framework, casting BBSE as roughly equivalent to MLLS for a particular choice of calibration method; and (iii) a decomposition of MLLS's finite-sample error into terms reflecting miscalibration and estimation error.
Quantization in Relative Gradient Angle Domain For Building Polygon Estimation
In this paper, we leverage the performance of CNNs, and propose a module that uses prior knowledge of building corners to create angular and concise building polygons from CNN segmentation outputs.
SSLIDE: Sound Source Localization for Indoors based on Deep Learning
This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space.
BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions
Therefore, we hope to develop a novel supervised learning method to learn the PPAs and DDAs effectively and thereby improve the prediction performance of the specific task of DPI.
Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition
To establish an effective features extracting and classification model is still a challenging task.
Estimating and Improving Fairness with Adversarial Learning
To tackle this issue, we propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system.
Instabilities of Offline RL with Pre-Trained Neural Representation
In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn) policies in scenarios where the data are collected from a distribution that substantially differs from that of the target policy to be evaluated.
On the Optimality of Batch Policy Optimization Algorithms
First, we introduce a class of confidence-adjusted index algorithms that unifies optimistic and pessimistic principles in a common framework, which enables a general analysis.
Unsupervised Learning of Multi-level Structures for Anomaly Detection
This paper first introduces a novel method to generate anomalous data by breaking up global structures while preserving local structures of normal data at multiple levels.
Mixture Proportion Estimation and PU Learning:A Modern Approach
Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE)---determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning---given such an estimate, learning the desired positive-versus-negative classifier.
A Pseudo Label-wise Attention Network for Automatic ICD Coding
The label-wise attention mechanism is widely used in automatic ICD coding because it can assign weights to every word in full Electronic Medical Records (EMR) for different ICD codes.
NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration
Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis.
On the Fairness of Swarm Learning in Skin Lesion Classification
Distributed and collaborative learning is an approach to involve training models in massive, heterogeneous, and distributed data sources, also known as nodes.
Mixture Proportion Estimation and PU Learning: A Modern Approach
Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE) -- determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning -- given such an estimate, learning the desired positive-versus-negative classifier.
Gridless DOA Estimation with Multiple Frequencies
Direction-of-arrival (DOA) estimation is widely applied in acoustic source localization.
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model
Such a problem is modeled as a Stackelberg game between the principal and the agent, where the principal announces a scoring rule that specifies the payment, and then the agent then chooses an effort level that maximizes her own profit and reports the information.
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge
As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments.
NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches
Recent work has focused on using computer vision and machine learning to automatically estimate dietary intake from food images, but the lack of comprehensive datasets with diverse viewpoints, modalities and food annotations hinders the accuracy and realism of such methods.
CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending
In fact, anomalous behaviors harming long context extrapolation exist between Rotary Position Embedding (RoPE) and vanilla self-attention unveiled by our work.
The Role of Chain-of-Thought in Complex Vision-Language Reasoning Task
The study explores the effectiveness of the Chain-of-Thought approach, known for its proficiency in language tasks by breaking them down into sub-tasks and intermediate steps, in improving vision-language tasks that demand sophisticated perception and reasoning.
Ranked #1 on
Visual Reasoning
on Winoground
NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation
Dietary intake estimation plays a crucial role in understanding the nutritional habits of individuals and populations, aiding in the prevention and management of diet-related health issues.
Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models
The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B).
COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics
The global ramifications of the COVID-19 pandemic remain significant, exerting persistent pressure on nations even three years after its initial outbreak.
Towards Establishing Dense Correspondence on Multiview Coronary Angiography: From Point-to-Point to Curve-to-Curve Query Matching
Coronary angiography is the gold standard imaging technique for studying and diagnosing coronary artery disease.
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection
Recent unsupervised anomaly detection methods often rely on feature extractors pretrained with auxiliary datasets or on well-crafted anomaly-simulated samples.
Non-uniform Array and Frequency Spacing for Regularization-free Gridless DOA
The DOA is retrieved using a Vandermonde decomposition on the Toeplitz matrix obtained from the solution of the SDP.
A Statistical Framework for Measuring AI Reliance
We argue that the current definition of appropriate reliance used in such research lacks formal statistical grounding and can lead to contradictions.
An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion
A total of 6 teams from 4 countries participated, whose proposed methods were evaluated on 16 images and two preoperative 3D models from two patients.
Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement
One of the main motivations of MTL is to develop neural networks capable of inferring multiple tasks simultaneously.
Ten computational challenges in human virome studies
In recent years, substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and diseases.
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data
Diagnosing rare diseases presents a common challenge in clinical practice, necessitating the expertise of specialists for accurate identification.
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation
Dataset distillation is an advanced technique aimed at compressing datasets into significantly smaller counterparts, while preserving formidable training performance.
Neural Ordinary Differential Equation based Sequential Image Registration for Dynamic Characterization
Leveraging Neural Ordinary Differential Equations (ODE) for registration, this extension work discusses how this framework can aid in the characterization of sequential biological processes.
