Search Results for author:
Found 24 papers, 9 papers with code
Doubly Robust Crowdsourcing
Large-scale labeled dataset is the indispensable fuel that ignites the AI revolution as we see today.
Supermartingale deflators in the absence of a numéraire
In this paper we study arbitrage theory of financial markets in the absence of a num\'eraire both in discrete and continuous time.
Unity Style Transfer for Person Re-Identification
To solve this problem, we propose a UnityStyle adaption method, which can smooth the style disparities within the same camera and across different cameras.
Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification
In this paper, we present a novel deep metric learning method to tackle the multi-label image classification problem.
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
The Private Aggregation of Teacher Ensembles (PATE) framework is one of the most promising recent approaches in differentially private learning.
City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones
Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions.
An Empirical Study of Vehicle Re-Identification on the AI City Challenge
We mainly focus on four points, i. e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge.
Vision-Language Navigation with Random Environmental Mixup
Then, we cross-connect the key views of different scenes to construct augmented scenes.
Ranked #38 on
Vision and Language Navigation
on VLN Challenge
Graph Convolution for Re-ranking in Person Re-identification
In this work, we propose a graph-based re-ranking method to improve learned features while still keeping Euclidean distance as the similarity metric.
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes
Stochastic processes are random variables with values in some space of paths.
CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations.
UFNRec: Utilizing False Negative Samples for Sequential Recommendation
To this end, we propose a novel method that can Utilize False Negative samples for sequential Recommendation (UFNRec) to improve model performance.
Global Optimization with Parametric Function Approximation
We consider the problem of global optimization with noisy zeroth order oracles - a well-motivated problem useful for various applications ranging from hyper-parameter tuning for deep learning to new material design.
Summative Student Course Review Tool Based on Machine Learning Sentiment Analysis to Enhance Life Science Feedback Efficacy
Often, student opinions are gathered with a general comment section that solicits their feelings towards their courses without polling specifics about course contents.
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking
In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing.
No-Regret Linear Bandits beyond Realizability
It achieves a near-optimal $\sqrt{T}$ regret for problems that the best-known regret is almost linear in time horizon $T$.
An Approximation Theory for Metric Space-Valued Functions With A View Towards Deep Learning
Motivated by the developing mathematics of deep learning, we build universal functions approximators of continuous maps between arbitrary Polish metric spaces $\mathcal{X}$ and $\mathcal{Y}$ using elementary functions between Euclidean spaces as building blocks.
Graph Convolution Based Efficient Re-Ranking for Visual Retrieval
In particular, the plain GCR is extended for cross-camera retrieval and an improved feature propagation formulation is presented to leverage affinity relationships across different cameras.
Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training
Most previous works either simply learn coarse-grained representations of the overall image and text, or elaborately establish the correspondence between image regions or pixels and text words.
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation
A powerful positive item augmentation is beneficial to address the sparsity issue, while few works could jointly consider both the accuracy and diversity of these augmented training labels.
Rational Decision-Making Agent with Internalized Utility Judgment
Central to the development of rationality is the construction of an internalized utility judgment, capable of assigning numerical utilities to each decision.
Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation
Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method.
Conditional Generative Representation for Black-Box Optimization with Implicit Constraints
This approach leverages a conditional variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision space and a simplified, constraint-free latent space.
Communication-Efficient Federated Non-Linear Bandit Optimization
Federated optimization studies the problem of collaborative function optimization among multiple clients (e. g. mobile devices or organizations) under the coordination of a central server.
