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Found 15 papers, 4 papers with code
WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations
Enhancing the attribution in large language models (LLMs) is a crucial task.
Efficient Post-training Quantization with FP8 Formats
Recent advances in deep learning methods such as LLMs and Diffusion models have created a need for improved quantization methods that can meet the computational demands of these modern architectures while maintaining accuracy.
On the Re-Solving Heuristic for (Binary) Contextual Bandits with Knapsacks
To the best of our knowledge, this is the first $\widetilde O(1)$ regret result in the CBwK problem regardless of information feedback models.
QuaLA-MiniLM: a Quantized Length Adaptive MiniLM
Our quantized length-adaptive MiniLM model (QuaLA-MiniLM) is trained only once, dynamically fits any inference scenario, and achieves an accuracy-efficiency trade-off superior to any other efficient approaches per any computational budget on the SQuAD1. 1 dataset (up to x8. 8 speedup with <1% accuracy loss).
Dynamic Budget Throttling in Repeated Second-Price Auctions
Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in.
No-regret Learning in Repeated First-Price Auctions with Budget Constraints
Recently the online advertising market has exhibited a gradual shift from second-price auctions to first-price auctions.
Flocking and Collision Avoidance for a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning
Developing the flocking behavior for a dynamic squad of fixed-wing UAVs is still a challenge due to kinematic complexity and environmental uncertainty.
Hybrid Differentially Private Federated Learning on Vertically Partitioned Data
We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized linear model (GLM) from vertically partitioned data with only a negligible cost, w. r. t.
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations
Federated Learning(FL) is a newly developed privacy-preserving machine learning paradigm to bridge data repositories without compromising data security and privacy.
Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games
Besides, taking advantages of the condition representation and matching mechanism of XCS, the heuristic policies and the opponent model can provide guidance for situations with similar feature representation.
Nostalgic Adam: Weighting more of the past gradients when designing the adaptive learning rate
NosAdam can be regarded as a fix to the non-convergence issue of Adam in alternative to the recent work of [Reddi et al., 2018].
Medical Synonym Extraction with Concept Space Models
In this paper, we present a novel approach for medical synonym extraction.
Practice in Synonym Extraction at Large Scale
Synonym extraction is an important task in natural language processing and often used as a submodule in query expansion, question answering and other applications.
