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Found 10 papers, 2 papers with code
HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2021 Efficiency Shared Task.
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact
Large language models (LLMs) excel in natural language processing but demand intensive computation.
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.
PanGu-$π$: Enhancing Language Model Architectures via Nonlinearity Compensation
We then demonstrate that the proposed approach is significantly effective for enhancing the model nonlinearity through carefully designed ablations; thus, we present a new efficient model architecture for establishing modern, namely, PanGu-$\pi$.
PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing
In this work, we develop a system that trained a trillion-parameter language model on a cluster of Ascend 910 AI processors and MindSpore framework, and present the language model with 1. 085T parameters named PanGu-{\Sigma}.
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations
Moreover, we show that contrastive learning fails to learn domain-invariant features, which limits its transferability.
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method
In this paper, to handle the problem and address the limitations of prior works, we propose a representation calibration method RCAL.
Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution
Portfolio management via reinforcement learning is at the forefront of fintech research, which explores how to optimally reallocate a fund into different financial assets over the long term by trial-and-error.
Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering
CAR-Transformer (1) revises each condition value based on the whole conversation and original conditions values, and (2) it encodes the revised conditions and utilizes the conditions embedding to select an answer.
Sub-Architecture Ensemble Pruning in Neural Architecture Search
Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.
