Search Results for author:
Found 15 papers, 3 papers with code
Automatic Keyphrase Generation by Incorporating Dual Copy Mechanisms in Sequence-to-Sequence Learning
However, we observed that most of the keyphrases are composed of some important words (seed words) in the source text, and if these words can be identified accurately and copied to create more keyphrases, the performance of the model might be improved.
Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation
This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems.
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
Reinforcement learning-based recommender systems have recently gained popularity.
Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform
We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment.
Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment
This paper presents TAG, an automatic system to derive optimized DNN training graph and its deployment onto any device topology, for expedited training in device- and topology- heterogeneous ML clusters.
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation
It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems.
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation
Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems.
Fast Lossless Neural Compression with Integer-Only Discrete Flows
In this work, we propose Integer-only Discrete Flows (IODF), an efficient neural compressor with integer-only arithmetic.
Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation
Recent advances have convinced that the ability of reinforcement learning to handle the dynamic process can be effectively applied in the interactive recommendation.
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads
The last decade has witnessed growth in the computational requirements for training deep neural networks.
Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses
A common approach to solve multilingual speech recognition is to run multiple monolingual ASR systems in parallel and rely on a language identification (LID) component that detects the input language.
Curls & Whey: Boosting Black-Box Adversarial Attacks
On the one hand, existing iterative attacks add noises monotonically along the direction of gradient ascent, resulting in a lack of diversity and adaptability of the generated iterative trajectories.
Textbook Question Answering Under Instructor Guidance With Memory Networks
The machine thus performs as an instructor to extract the essay-level contradictions as the Guidance.
AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine
We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models.
