随笔分类 - Neural Networks
摘要：目录概符号说明4-bit FQTLearned Step Size QuantizationHadamard QuantizationBit Splitting and Leverage Score Sampling代码 Xi H., Li C., Chen J. and Zhu J. Traini
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摘要：目录概主要内容Radix-4 FP4 formatGradScaleTwo-Phase Rounding (TPR) Sun X., Wang N., Chen C., Ni J., Agrawal A., Cui X., Venkataramani S. and Maghraoui K. E. a
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摘要：目录概Range Batch Normalization代码 Banner R., Hubara I., Hoffer E. and Soudry D. Scalable methods for 8-bit training of neural networks. NeurIPS, 2018. 概
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摘要：目录概主要内容 Wang N., Choi J., Brand D., Chen C. and Gopalakrishnan K. Training deep neural networks with 8-bit floating point numbers. NeurIPS, 2018. 概 本文
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摘要：目录概主要内容 Narang S., Diamos G., Elsen E., Micikevicius P., Alben J., Garcia D., Ginsburg B., Houston M., Kuchaiev O., Venkatesh G. and Wu H. Mixed preci
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摘要：目录概HAWQ (Hessian AWare Quantization) Dong Z., Yao Z., Gholami A., Mahoney M. W. and Keutzer K. HAWQ: Hessian aware quantization of neural networks wit
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摘要：目录概主要内容 Choi J., Wang Z., Venkataramani S., Chuang P. I., Srinivasan V. and Gopalakrishnan K. PACT: Parameterized clipping activation for quantized ne
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摘要：目录概主要思想代码 Zhou A., Yao A., Guo Y., Xu L. and Chen Y. Incremental network quantization: Towards lossless cnns with low-precision weights. ICLR, 2017. 概
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摘要：目录概Logarithmic Unbiased Quantization代码 Chmiel B., Banner R., Hoffer E., Yaacov H. B. and Soundry D. Accurate neural training with 4-bit matrix multipl
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摘要：目录概AWQ代码 Lin J., Tang J., Tang H., Yang S., Chen W., Wang W., Xiao G., Dang X., Gan C. and Han S. AWQ: Activation-aware weight quantization for llm co
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摘要：目录概UoT代码 Hu Z., Liu C., Feng X., Zhao Y., Ng S., Luu A. T., He J., Koh P. W. and Hooi B. Uncertainty of thoughts: Uncertainty-aware planning enhances
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摘要：目录概符号说明MotivationNeo-GNN代码 Neo-GNNs: Neighborhood overlap-aware graph neural networks for link prediction. NeurIPS, 2021. 概 一种计算上相对高效的, 同时利用结构信息和特征信息的
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摘要：目录概BACON代码 [Yang Z., Feng R., et al. BACON: Supercharge your vlm with bag-of-concept graph to mitigate hallucinations. 2024.] 概 本文提出了一种新的数据格式: BACON (
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摘要：目录概ID-GNN You J., Gomoes-Selman J., Ying R. and Leskovec J. Identity-aware graph neural networks. AAAI, 2021. 概 提出了一种能够超越 1-WL-Test 的 GNN. ID-GNN ID-G
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摘要：目录概符号说明Homophily on Feature Aspect [1] Chen Y., Luo Y., Tang J., Yang L., Qiu S., Wang C. and Cao X. LSGNN: Towards general graph neural network in no
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摘要：目录概MoE训练 Shazeer N., Mirhoseini A., Maziarz K., Davis A., Le Q., Hinton G. and Dean J. Outrageously large neural networks: The sparsely-gated mixture-
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摘要：目录概符号说明MotivationNBFNet代码 Zhu Z., Zhang Z., Xhonneux L. and Tang J. Neural Bellman-Ford networks: A general graph neural network framework for link pr
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摘要：目录概符号说明PA-GNN Yang Y., Liang Y. and Zhang M. PA-GNN: Parameter-adaptive graph neural networks. ICML workshop, 2022. 概 一个自适应学习 GNN layer weights 的方法. 符
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摘要：目录概符号说明Popular homophily measures理想的准则现有的 metrics 的分析 Platonov O., Kuznedelev D., Babenko A. and Prokhorenkova L. Characterizing graph datasets for no
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摘要：目录概符号说明Homophily metricsPost-aggregation node similarity matrix代码 Luan S., Hua C., Lu Q., Zhu J., Zhao M., Zhang S., Chang X. and Precup D. Revisiting
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