Search Results for author:
Found 10 papers, 2 papers with code
DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level
Recommendation systems play a crucial role in various domains, suggesting items based on user behavior. However, the lack of transparency in presenting recommendations can lead to user confusion.
Not all Layers of LLMs are Necessary during Inference
To answer this question, we first indicate that Not all Layers are Necessary during Inference by statistically analyzing the activated layers across tasks.
V2VSSC: A 3D Semantic Scene Completion Benchmark for Perception with Vehicle to Vehicle Communication
Extensive experiments demonstrate that by leveraging V2V communication, the SSC performance can be increased by 8. 3% on geometric metric IoU and 6. 0% mIOU.
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS).
FLM-101B: An Open LLM and How to Train It with $100K Budget
We demonstrate that a 101B-parameter LLM with 0. 31T tokens can be trained with a budget of 100K US dollars.
nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales
With around 14% of the one-time pre-training cost, we can accurately forecast the loss for models up to 52B.
RetroGraph: Retrosynthetic Planning with Graph Search
We observe that the same intermediate molecules are visited many times in the searching process, and they are usually independently treated in previous tree-based methods (e. g., AND-OR tree search, Monte Carlo tree search).
Ranked #2 on
Multi-step retrosynthesis
on USPTO-190
GEO satellites on-orbit repairing mission planning with mission deadline constraint using a large neighborhood search-genetic algorithm
This paper proposed a novel large neighborhood search-adaptive genetic algorithm (LNS-AGA) for many-to-many on-orbit repairing mission planning of geosynchronous orbit (GEO) satellites with mission deadline constraint.
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation
This linear model is then utilized to reduce the redundant information in the left and right road images.
Fast Constraint Propagation for Image Segmentation
The experimental results demonstrate the promising performance of the proposed method for segmentation with selectively propagated constraints.
