Search Results for author:
Found 13 papers, 2 papers with code
GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training
(2) The diverse semantics of side information that describes items and users from multi-level in a context different from recommendation systems.
HARL: Hierarchical Adaptive Reinforcement Learning Based Auto Scheduler for Neural Networks
However, due to the gigantic search space and lack of intelligent search guidance, current auto-schedulers require hours to days of tuning time to find the best-performing tensor program for the entire neural network.
Time Series Domain Adaptation via Sparse Associative Structure Alignment
To reduce the difficulty in the discovery of causal structure, we relax it to the sparse associative structure and propose a novel sparse associative structure alignment model for domain adaptation.
Speech Fusion to Face: Bridging the Gap Between Human's Vocal Characteristics and Facial Imaging
While deep learning technologies are now capable of generating realistic images confusing humans, the research efforts are turning to the synthesis of images for more concrete and application-specific purposes.
Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems
However, the mainstream domain adaptation methods cannot achieve ideal performance on time series data, because most of them focus on static samples and even the existing time series domain adaptation methods ignore the properties of time series data, such as temporal causal mechanism.
Causal Discovery with Cascade Nonlinear Additive Noise Models
In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect.
Data Driven Chiller Plant Energy Optimization with Domain Knowledge
Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of over-simplified assumptions, over the equipments.
Causal Discovery from Discrete Data using Hidden Compact Representation
In this paper we make an attempt to find a way to solve this problem by assuming a two-stage causal process: the first stage maps the cause to a hidden variable of a lower cardinality, and the second stage generates the effect from the hidden representation.
An Encoder-Decoder Framework Translating Natural Language to Database Queries
These techniques help the neural network better focus on understanding semantics of operations in natural language and save the efforts on SQL grammar learning.
SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guarantee
We theoretically prove that SADA always reduces the scales of problems without sacrifice on accuracy, under the condition of local causal sparsity and reliable conditional independence tests.
Distributed Representations for Building Profiles of Users and Items from Text Reviews
In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems.
Component-Based Distributed Framework for Coherent and Real-Time Video Dehazing
Traditional dehazing techniques, as a well studied topic in image processing, are now widely used to eliminate the haze effects from individual images.
Convex Optimization for Linear Query Processing under Approximate Differential Privacy
This paper points out that under ($\epsilon$, $\delta$)-differential privacy, the optimal solution of the above constrained optimization problem in search of a suitable strategy can be found, rather surprisingly, by solving a simple and elegant convex optimization program.
