Search Results for author:
Found 10 papers, 3 papers with code
ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewarding
ReARTeR addresses three core challenges: (1) misalignment between PRM and PEM, tackled through off-policy preference learning; (2) bias in PRM training data, mitigated by balanced annotation methods and stronger annotations for challenging examples; and (3) early-step bias in PRM, resolved through a temporal-difference-based look-ahead search strategy.
Anatomizing Deep Learning Inference in Web Browsers
The gap on mobile CPU and mobile GPU is 15. 8 times and 7. 8 times, respectively.
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine learning lifecycle, from training to deployment.
MUSIC Algorithm for IRS-Assisted AOA Estimation
In this paper, we consider a more challenging AOA estimation setup in the intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system, where LOS paths do not exist between the BS and the users, while the users' signals can be transmitted to the BS merely via their LOS paths to the IRS as well as the LOS path from the IRS to the BS.
A Heterogeneous 6G Networked Sensing Architecture with Active and Passive Anchors
In this paper, we show that the distance between a target and its associated IRS can be indirectly estimated based on the length of the BS-target-BS path and the BS-target-IRS-BS path.
Trilateration-Based Device-Free Sensing: Two Base Stations and One Passive IRS Are Sufficient
The classic trilateration technique can localize each target based on its distances to three anchors with known coordinates.
Exploiting Temporal Side Information in Massive IoT Connectivity
In particular, we propose to leverage the temporal correlation in device activity, e. g., a device active in the previous coherence block is more likely to be still active in the current coherence block, to improve the detection and estimation performance.
On Massive IoT Connectivity with Temporally-Correlated User Activity
In particular, we propose to leverage the temporal correlation in user activity, i. e., a device active at the previous time slot is more likely to be still active at the current moment, to improve the detection performance.
Action Detection
Activity Detection
Information Theory
Signal Processing
Information Theory
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Based on the data and the platform, we conduct extensive experiments to compare the performance of state-of-the-art FL algorithms under heterogeneity-aware and heterogeneity-unaware settings.
Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints
We study the combinatorial sleeping multi-armed semi-bandit problem with long-term fairness constraints~(CSMAB-F).
