Search Results for author:
Found 16 papers, 11 papers with code
Wukong: Towards a Scaling Law for Large-Scale Recommendation
Scaling laws play an instrumental role in the sustainable improvement in model quality.
Diffusive Gibbs Sampling
The inadequate mixing of conventional Markov Chain Monte Carlo (MCMC) methods for multi-modal distributions presents a significant challenge in practical applications such as Bayesian inference and molecular dynamics.
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation
Human annotator simulation (HAS) serves as a cost-effective substitute for human evaluation such as data annotation and system assessment.
Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking
In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i. e. ads clicks and ads quality events) and their task relations.
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
This work extends the theory of identifiability in supervised learning by considering the consequences of having access to a distribution of tasks.
Neural Characteristic Activation Value Analysis for Improved ReLU Network Feature Learning
This work examines the characteristic activation values of individual ReLU units in neural networks.
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta-learning and conventional deep kernel learning.
Optimal Client Sampling for Federated Learning
We show that importance can be measured using only the norm of the update and give a formula for optimal client participation.
Deep Learning Recommendation Model for Personalization and Recommendation Systems
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.
To Ensemble or Not Ensemble: When does End-To-End Training Fail?
An interesting question is whether this trend will continue-are there any clear failure cases for E2E training?
Multi-Scale Convolutional Neural Networks for Time Series Classification
These methods are ad-hoc and separate the feature extraction part with the classification part, which limits their accuracy performance.
Fast Distributed k-Center Clustering with Outliers on Massive Data
Clustering large data is a fundamental problem with a vast number of applications.
Optimal Action Extraction for Random Forests and Boosted Trees
To address this problem, we present a novel framework to post-process any ATM classifier to extract an optimal actionable plan that can change a given input to a desired class with a minimum cost.
Compressing Convolutional Neural Networks
Convolutional neural networks (CNN) are increasingly used in many areas of computer vision.
Compressing Neural Networks with the Hashing Trick
As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models.
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing
Our second contribution is to derive a practical algorithm based on this reduction.
