Search Results for author:
Found 25 papers, 11 papers with code
Better Computer Go Player with Neural Network and Long-term Prediction
Against human players, the newest versions, darkfores2, achieve a stable 3d level on KGS Go Server as a ranked bot, a substantial improvement upon the estimated 4k-5k ranks for DCNN reported in Clark & Storkey (2015) based on games against other machine players.
The UCR Time Series Archive
This paper introduces and will focus on the new data expansion from 85 to 128 data sets.
GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF
In this work, we tackle 6-DoF grasp detection for transparent and specular objects, which is an important yet challenging problem in vision-based robotic systems, due to the failure of depth cameras in sensing their geometry.
Time-based Sequence Model for Personalization and Recommendation Systems
In this paper we develop a novel recommendation model that explicitly incorporates time information.
Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder
Topic models have been prevailing for many years on discovering latent semantics while modeling long documents.
Semantic Amodal Segmentation
Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels.
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models
EndoKED automates the transformation of raw colonoscopy records into image datasets with pixel-level annotation.
Orthogonal Temporal Interpolation for Zero-Shot Video Recognition
We propose a model called OTI for ZSVR by employing orthogonal temporal interpolation and the matching loss based on VLMs.
Ranked #1 on
Zero-Shot Action Recognition
on UCF101
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability.
Flexible Error Mitigation of Quantum Processes with Data Augmentation Empowered Neural Model
Neural networks have shown their effectiveness in various tasks in the realm of quantum computing.
Admissible Time Series Motif Discovery with Missing Data
Although there has been more than a decade of extensive research, there is still no technique to allow the discovery of time series motifs in the presence of missing data, despite the well-documented ubiquity of missing data in scientific, industrial, and medical datasets.
Interactive Reinforcement Learning for Object Grounding via Self-Talking
In this paper, we introduce an interactive training method to improve the natural language conversation system for a visual grounding task.
Representation Learning by Reconstructing Neighborhoods
Since its introduction, unsupervised representation learning has attracted a lot of attention from the research community, as it is demonstrated to be highly effective and easy-to-apply in tasks such as dimension reduction, clustering, visualization, information retrieval, and semi-supervised learning.
Neighbor-encoder
By reformulating the representation learning problem as a neighbor reconstruction problem, domain knowledge can be easily incorporated with appropriate definition of similarity or distance between objects.
Learning Two-Time-Scale Representations For Large Scale Recommendations
We propose a surprisingly simple but effective two-time-scale (2TS) model for learning user representations for recommendation.
Flexible learning of quantum states with generative query neural networks
Existing networks are typically trained with experimental data gathered from the specific quantum state that needs to be characterized.
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications.
Explainable Fairness in Recommendation
In this paper, we study the problem of explainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology.
Quantum Similarity Testing with Convolutional Neural Networks
The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous-variable quantum systems.
Robust Time Series Chain Discovery with Incremental Nearest Neighbors
Inspired by a recent work that tracks how the nearest neighbor of a time series subsequence changes over time, we introduce a new TSC definition which is much more robust to noise in the data, in the sense that they can better locate the evolving patterns while excluding the non-evolving ones.
EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)
In addition, the released dataset can be used to perform 'stress' tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis.
Learning quantum properties from short-range correlations using multi-task networks
Through numerical experiments, we show that multi-task learning can be applied to sufficiently regular states to predict global properties, like string order parameters, from the observation of short-range correlations, and to distinguish between quantum phases that cannot be distinguished by single-task networks.
MaskClustering: View Consensus based Mask Graph Clustering for Open-Vocabulary 3D Instance Segmentation
Through iterative clustering of masks showing high view consensus, we generate a series of clusters, each representing a distinct 3D instance.
3D Instance Segmentation
3D Open-Vocabulary Instance Segmentation
+6
SportQA: A Benchmark for Sports Understanding in Large Language Models
A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP).
Invariant Test-Time Adaptation for Vision-Language Model Generalization
Vision-language foundation models have exhibited remarkable success across a multitude of downstream tasks due to their scalability on extensive image-text paired datasets.
