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Found 11 papers, 2 papers with code
BiSup: Bidirectional Quantization Error Suppression for Large Language Models
As the size and context length of Large Language Models (LLMs) grow, weight-activation quantization has emerged as a crucial technique for efficient deployment of LLMs.
Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid Framework
Industry-wide nuclear power plant operating experience is a critical source of raw data for performing parameter estimations in reliability and risk models.
Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation
The multi-funnel setup effectively balances between coverage and relevance.
An Asynchronous Updating Reinforcement Learning Framework for Task-oriented Dialog System
The errors from DST might misguide the dialog policy, and the system action brings extra difficulties for the DST module.
Reward Shaping for User Satisfaction in a REINFORCE Recommender
How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction?
A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
We find some new linguistic phenomena and interactive manners in SSTOD which raise critical challenges of building dialog agents for the task.
Ranked #1 on
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Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble
In this paper, we present the design of error-resilient machine learning architectures by employing a distributed machine learning framework referred to as classifier ensemble (CE).
Understanding the Energy and Precision Requirements for Online Learning
Lower bounds on the data precision are derived in terms of the the desired classification accuracy and precision of the hyperparameters used in the classifier.
Elastic regularization in restricted Boltzmann machines: Dealing with $p\gg N$
Our novel model is a promising method for future cancer data analysis.
Partitioning Large Scale Deep Belief Networks Using Dropout
Deep learning methods have shown great promise in many practical applications, ranging from speech recognition, visual object recognition, to text processing.
