Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
Neural Information Processing Systems
Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.
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MatFormer: Nested Transformer for Elastic Inference Devvrit
Neural Information Processing Systems
Feed Forward Network (FFN) block structure within a standard Transformer model.
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TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs Julia Gastinger 1,2,6 Shenyang Huang 1,4 Mikhail Galkin
Neural Information Processing Systems
However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols.
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Learning Partitions from Context
Neural Information Processing Systems
Modern machine learning systems are able to learn extremely complicated relations from data.
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MiniCache: KV Cache Compression in Depth Dimension for Large Language Models
Neural Information Processing Systems
A critical approach for efficiently deploying computationally demanding large language models (LLMs) is Key-V alue (KV) caching.
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