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Found 14 papers, 5 papers with code
Enhancing Learned Knowledge in LoRA Adapters Through Efficient Contrastive Decoding on Ascend NPUs
In this paper, we introduce Contrastive LoRA Decoding (CoLD), a novel decoding framework designed to maximize the use of task-specific knowledge in LoRA-adapted models, resulting in better downstream performance.
Efficiently Serving Large Multimodal Models Using EPD Disaggregation
Large Multimodal Models (LMMs) extend Large Language Models (LLMs) by handling diverse inputs such as images, audio, and video, but at the cost of adding a multimodal encoding stage that increases both computational and memory overhead.
Learn2Aggregate: Supervised Generation of Chvátal-Gomory Cuts Using Graph Neural Networks
We present $\textit{Learn2Aggregate}$, a machine learning (ML) framework for optimizing the generation of Chv\'atal-Gomory (CG) cuts in mixed integer linear programming (MILP).
DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning
While in-context Learning (ICL) has proven to be an effective technique to improve the performance of Large Language Models (LLMs) in a variety of complex tasks, notably in translating natural language questions into Structured Query Language (NL2SQL), the question of how to select the most beneficial demonstration examples remains an open research problem.
SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encoder
Furthermore, compared to these competitive models, our proposed encoder enhances the downstream performance of NL2SQL models in 1-shot in-context learning scenarios by 1-2\% for GPT-3. 5-turbo, 4-8\% for CodeLlama-7B, and 2-3\% for CodeLlama-13B.
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.
Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process
The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR).
Knowledge-Injected Federated Learning
Federated learning is an emerging technique for training models from decentralized data sets.
A dual approach for federated learning
We study the federated optimization problem from a dual perspective and propose a new algorithm termed federated dual coordinate descent (FedDCD), which is based on a type of coordinate descent method developed by Necora et al.[Journal of Optimization Theory and Applications, 2017].
Fair and efficient contribution valuation for vertical federated learning
We show that VerFedSV not only satisfies many desirable properties for fairness but is also efficient to compute, and can be adapted to both synchronous and asynchronous vertical federated learning algorithms.
Improving Fairness for Data Valuation in Horizontal Federated Learning
The success of federated learning depends largely on the participation of data owners.
Achieving Model Fairness in Vertical Federated Learning
To solve it in a federated and privacy-preserving manner, we consider the equivalent dual form of the problem and develop an asynchronous gradient coordinate-descent ascent algorithm, where some active data parties perform multiple parallelized local updates per communication round to effectively reduce the number of communication rounds.
Fast convergence of stochastic subgradient method under interpolation
We prove that SSGD converges, respectively, with rates $O(1/\epsilon)$ and $O(\log(1/\epsilon))$ for convex and strongly-convex objectives when interpolation holds.
Polar Deconvolution of Mixed Signals
The signal demixing problem seeks to separate a superposition of multiple signals into its constituent components.
