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Found 9 papers, 4 papers with code
ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding
Additionally, it introduces a dynamic token tree generation algorithm to balance the computation and parallelism of the verification phase in real-time and maximize the overall efficiency across different batch sizes, sequence lengths, and tasks, etc.
AttentionLego: An Open-Source Building Block For Spatially-Scalable Large Language Model Accelerator With Processing-In-Memory Technology
Large language models (LLMs) with Transformer architectures have become phenomenal in natural language processing, multimodal generative artificial intelligence, and agent-oriented artificial intelligence.
PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics
PiML (read $\pi$-ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics.
Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions
In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI).
Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
We propose the local linear profile plot and other visualization methods for interpretation and diagnostics, and an effective merging strategy for network simplification.
Hyperparameter Optimization via Sequential Uniform Designs
Hyperparameter optimization (HPO) plays a central role in the automated machine learning (AutoML).
An Effective and Efficient Initialization Scheme for Training Multi-layer Feedforward Neural Networks
Network initialization is the first and critical step for training neural networks.
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions
The lack of interpretability is an inevitable problem when using neural network models in real applications.
Enhancing Explainability of Neural Networks through Architecture Constraints
It leads to an explainable neural network (xNN) with the superior balance between prediction performance and model interpretability.
