40 citations · 68 across the 14 of their papers we have counts for
14 papers
DASH: Divergence-Adaptive Supervision Horizons for On-Policy Self-Distillation of Reasoning Models
ZhiYan Hou, Xinyu Tang, Hongyan An +9
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models using automatically verifiable outcome signals, but these signals…
CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens for Vision-Language Models
Yu Chen, Xiaohong Li, Xiaole Wang +3
In video understanding, vision-language models (VLMs) must ingest massive numbers of visual tokens, causing the computational and memory cost of the prefill stage to rise sharply.…
DUET: Joint Exploration of User Item Profiles in Recommendation System
Yue Chen, Yifei Sun, Lu Wang +17
Traditional recommendation systems represent users and items as dense vectors and learn to align them in a shared latent space for relevance estimation. Recent LLM-based recommende…
MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens
Yu Chen, Runkai Chen, Sheng Yi +9
Long-term memory is a cornerstone of human intelligence. Enabling AI to process lifetime-scale information remains a long-standing pursuit in the field. Due to the constraints of f…
AdNanny: One Reasoning LLM for All Offline Ads Recommendation Tasks
Nan Hu, Han Li, Jimeng Sun +16
Large Language Models (LLMs) have shown strong capabilities in Natural Language Understanding and Generation, but deploying them directly in online advertising systems is often imp…
RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning
Mingrui Wu, Lu Wang, Pu Zhao +14
Despite recent progress in text-to-image (T2I) generation, existing models often struggle to faithfully capture user intentions from short and under-specified prompts. While prior…
Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanism
Jun Yin, Zhengxin Zeng, Mingzheng Li +13
Owing to the unprecedented capability in semantic understanding and logical reasoning, large language models (LLMs) have shown fantastic potential in developing next-generation seq…
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings
Shitao Xiao, Zheng Liu, Weihao Han +10
Vector quantization (VQ) based ANN indexes, such as Inverted File System (IVF) and Product Quantization (PQ), have been widely applied to embedding based document retrieval thanks…
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
Shitao Xiao, Zheng Liu, Weihao Han +9
Ad-hoc search calls for the selection of appropriate answers from a massive-scale corpus. Nowadays, the embedding-based retrieval (EBR) becomes a promising solution, where deep lea…
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search
Jianjin Zhang, Zheng Liu, Weihao Han +9
Embedding based retrieval (EBR) is a fundamental building block in many web applications. However, EBR in sponsored search is distinguished from other generic scenarios and technic…
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
Yunbo Wang, Haixu Wu, Jianjin Zhang +4
The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical context, where the visual dynamics are believed to have modular s…
Towards Good Practices of U-Net for Traffic Forecasting
Jingwei Xu, Jianjin Zhang, Zhiyu Yao +1
This technical report presents a solution for the 2020 Traffic4Cast Challenge. We consider the traffic forecasting problem as a future frame prediction task with relatively weak te…
Learning Fast Matching Models from Weak Annotations
Xue Li, Zhipeng Luo, Hao Sun +5
This paper proposes a novel training scheme for fast matching models in Search Ads, which is motivated by the real challenges in model training. The first challenge stems from the…
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
Yunbo Wang, Jianjin Zhang, Hongyu Zhu +3
Natural spatiotemporal processes can be highly non-stationary in many ways, e.g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel…