Search Results for author:
Found 12 papers, 4 papers with code
Monolith: Real Time Recommendation System With Collisionless Embedding Table
In this paper, we present Monolith, a system tailored for online training.
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU
Our experiments show that previous scaling rules fail in the training of CTR prediction neural networks.
Correlated Variational Auto-Encoders
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data.
The Variational Predictive Natural Gradient
Unlike traditional natural gradients for variational inference, this natural gradient accounts for the relationship between model parameters and variational parameters.
Subgoal Discovery for Hierarchical Dialogue Policy Learning
Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals.
Initialization and Coordinate Optimization for Multi-way Matching
We consider the problem of consistently matching multiple sets of elements to each other, which is a common task in fields such as computer vision.
Item Recommendation with Variational Autoencoders and Heterogenous Priors
In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback.
Learning Correlated Latent Representations with Adaptive Priors
Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data.
Active Multitask Learning with Committees
The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches.
Toward Annotator Group Bias in Crowdsourcing
We conduct experiments on both synthetic and real-world datasets.
Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy
This paper introduces a multifaceted methodology for fine-tuning and evaluating large language models (LLMs) for specialized monetization tasks.
ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers
The emergence of Large Language Models (LLMs) such as ChatGPT and LLaMA encounter limitations in domain-specific tasks, with these models often lacking depth and accuracy in specialized areas, and exhibiting a decrease in general capabilities when fine-tuned, particularly analysis ability in small sized models.
