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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Can LLMs Implicitly Learn Numeric Parameter Constraints in Data Science APIs?
Neural Information Processing Systems
However, this assumption has not been rigorously studied in the literature.
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LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
Neural Information Processing Systems
GPUs remains a significant challenge.
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StepbaQ: Stepping backward as Correction for Quantized Diffusion Models
Neural Information Processing Systems
Quantization of diffusion models has attracted considerable attention due to its potential to enable various applications on resource-constrained mobile devices.
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FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling
Neural Information Processing Systems
To overcome the limitations of autoregressive methods (e.g., error accumulation), recent works
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