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Found 15 papers, 1 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.
FastAttention: Extend FlashAttention2 to NPUs and Low-resource GPUs
In this work, we propose FastAttention which pioneers the adaptation of FlashAttention series for NPUs and low-resource GPUs to boost LLM inference efficiency.
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems
Artificial intelligence methods are being increasingly applied across various domains, but their often opaque nature has raised concerns about accountability and trust.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning
To address these challenges, we introduce a novel skeleton-based training framework (C$^2$VL) based on Cross-modal Contrastive learning that uses the progressive distillation to learn task-agnostic human skeleton action representation from the Vision-Language knowledge prompts.
Scaled Prompt-Tuning for Few-Shot Natural Language Generation
The increasingly Large Language Models (LLMs) demonstrate stronger language understanding and generation capabilities, while the memory demand and computation cost of fine-tuning LLMs on downstream tasks are non-negligible.
Genetic heterogeneity analysis using genetic algorithm and network science
Through genome-wide association studies (GWAS), disease susceptible genetic variables can be identified by comparing the genetic data of individuals with and without a specific disease.
Evolutionary approaches to explainable machine learning
We then focus on how evolutionary computing can be used in XAI/XML, and review some approaches which incorporate EC techniques.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Phenotype Search Trajectory Networks for Linear Genetic Programming
Genotype-to-phenotype mappings translate genotypic variations such as mutations into phenotypic changes.
Empirical Evaluation of Post-Training Quantization Methods for Language Tasks
We further explore the limit of quantization bit and show that OCS could quantize BERT-Base and BERT-Large to 3-bits and retain 98% and 96% of the performance on the GLUE benchmark accordingly.
A spectral-spatial fusion anomaly detection method for hyperspectral imagery
In hyperspectral, high-quality spectral signals convey subtle spectral differences to distinguish similar materials, thereby providing unique advantage for anomaly detection.
DiriNet: A network to estimate the spatial and spectral degradation functions
To learn the spatial response function and the point spread function from the image pairs to be fused, we propose a Dirichlet network, where both functions are properly constrained.
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings
A key property of this representation is that there are multiple representations of a network which can be obtained by permuting the order of the neurons.
Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation
Recently, various approaches with Generative Adversarial Nets (GANs) have also been proposed.
Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions
While the behavior of SGD is well understood in the convex learning setting, the existing theoretical results for SGD applied to nonconvex objective functions are far from mature.
Consistency Analysis of an Empirical Minimum Error Entropy Algorithm
The error entropy consistency, which requires the error entropy of the learned function to approximate the minimum error entropy, is shown to be always true if the bandwidth parameter tends to 0 at an appropriate rate.
