Search Results for author:
Found 26 papers, 8 papers with code
MobileFlow: A Multimodal LLM For Mobile GUI Agent
Currently, the integration of mobile Graphical User Interfaces (GUIs) is ubiquitous in most people's daily lives.
MATEval: A Multi-Agent Discussion Framework for Advancing Open-Ended Text Evaluation
Experimental results show that our framework outperforms existing open-ended text evaluation methods and achieves the highest correlation with human evaluation, which confirms the effectiveness and advancement of our framework in addressing the uncertainties and instabilities in evaluating LLMs-generated text.
DEE: Dual-stage Explainable Evaluation Method for Text Generation
Automatic methods for evaluating machine-generated texts hold significant importance due to the expanding applications of generative systems.
Sparse Transformer with Local and Seasonal Adaptation for Multivariate Time Series Forecasting
To address this, we propose a Dozer Attention mechanism consisting of three sparse components: (1) Local, each query exclusively attends to keys within a localized window of neighboring time steps.
WIP: Development of a Student-Centered Personalized Learning Framework to Advance Undergraduate Robotics Education
This paper presents a work-in-progress on a learn-ing system that will provide robotics students with a personalized learning environment.
Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting
Multivariate Time Series (MTS) forecasting involves modeling temporal dependencies within historical records.
Continuous odor profile monitoring to study olfactory navigation in small animals
Olfactory navigation is observed across species and plays a crucial role in locating resources for survival.
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs.
Data Imputation for Multivariate Time Series Sensor Data with Large Gaps of Missing Data
Imputation of missing sensor-collected data is often an important step prior to machine learning and statistical data analysis.
Conditional Generation Net for Medication Recommendation
Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics.
HybridGazeNet: Geometric model guided Convolutional Neural Networks for gaze estimation
As a critical cue for understanding human intention, human gaze provides a key signal for Human-Computer Interaction(HCI) applications.
Multi-scale 2D Representation Learning for weakly-supervised moment retrieval
Specifically, we first construct a two-dimensional map for each temporal scale to capture the temporal dependencies between candidates.
What Makes for Hierarchical Vision Transformer?
Recent studies indicate that hierarchical Vision Transformer with a macro architecture of interleaved non-overlapped window-based self-attention \& shifted-window operation is able to achieve state-of-the-art performance in various visual recognition tasks, and challenges the ubiquitous convolutional neural networks (CNNs) using densely slid kernels.
You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection
Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure?
Ranked #30 on
Object Detection
on COCO-O
Identifying the origin of the non-monotonic thickness dependence of spin-orbit torques and interfacial Dzyaloshinskii-Moriya interaction in a ferrimagnetic insulator heterostructure
We find that the non-monotonic SOT efficiency as a function of the magnetic layer thickness is not consistent with the 1/thickness dependence expected from a simple interfacial SOT mechanism.
Materials Science
Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation
We note that 2D pose estimation task is highly dependent on the contextual relationship between image patches, thus we introduce a self-supervised method for pretraining 2D pose estimation networks.
MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish Images with Latent Variable Model
Nowadays, driven by the increasing concern on diet and health, food computing has attracted enormous attention from both industry and research community.
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments.
RecSim: A Configurable Simulation Platform for Recommender Systems
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users.
Learning Enhanced Resolution-wise features for Human Pose Estimation
Specifically, RAM learns a group of weights to represent the different importance of feature maps across resolutions, and the GPR gradually merges every two feature maps from low to high resolutions to regress final human keypoint heatmaps.
Exploiting Offset-guided Network for Pose Estimation and Tracking
Human pose estimation has witnessed a significant advance thanks to the development of deep learning.
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
(i) We develop SLATEQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates.
Clustering and Inference From Pairwise Comparisons
We propose an efficient algorithm that accurately estimates the individual preferences for almost all users, if there are $r \max \{m, n\}\log m \log^2 n$ pairwise comparisons per type, which is near optimal in sample complexity when $r$ only grows logarithmically with $m$ or $n$.
Collaborative Filtering with Information-Rich and Information-Sparse Entities
In particular, we consider both the clustering model, where only users (or items) are clustered, and the co-clustering model, where both users and items are clustered, and further, we assume that some users rate many items (information-rich users) and some users rate only a few items (information-sparse users).
Jointly Clustering Rows and Columns of Binary Matrices: Algorithms and Trade-offs
In standard clustering problems, data points are represented by vectors, and by stacking them together, one forms a data matrix with row or column cluster structure.
Learning Loosely Connected Markov Random Fields
We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm for learning the underlying graph structure.
