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Found 14 papers, 1 papers with code
Collaboration by Competition: Self-coordinated Knowledge Amalgamation for Multi-talent Student Learning
To this end, we propose a self-coordinate knowledge amalgamation network (SOKA-Net) for learning the multi-talent student model.
Harness Local Rewards for Global Benefits: Effective Text-to-Video Generation Alignment with Patch-level Reward Models
To address this issue, we propose a post-training strategy for VGMs, HALO, which explicitly incorporates local feedback from a patch reward model, providing detailed and comprehensive training signals with the video reward model for advanced VGM optimization.
Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model
We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance.
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees
Specifically, we first provide a novel dynamic regret analysis for an existing projection-free method named $\text{BOGD}_\text{IP}$, and establish an $\mathcal{O}(T^{3/4}(1+P_T))$ dynamic regret bound, where $P_T$ denotes the path-length of the comparator sequence.
Distilling Task-specific Logical Rules from Large Pre-trained Models
Specifically, we borrow recent prompt-based language models as the knowledge expert to yield initial seed rules, and based on the formed high-quality instance pool that acts as an intermediary role, we keep teaching the expert to fit our task and learning task-specific logical rules.
Shape Controllable Virtual Try-on for Underwear Models
Existing 2D image-based virtual try-on methods aim to transfer a target clothing image onto a reference person, which has two main disadvantages: cannot control the size and length precisely; unable to accurately estimate the user's figure in the case of users wearing thick clothes, resulting in inaccurate dressing effect.
SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis
Previous studies show effective of pre-trained language models for sentiment analysis.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Path-based Deep Network for Candidate Item Matching in Recommenders
PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content.
A Hybrid Bandit Framework for Diversified Recommendation
Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set.
Pre-training Graph Transformer with Multimodal Side Information for Recommendation
Side information of items, e. g., images and text description, has shown to be effective in contributing to accurate recommendations.
Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers
In this paper, we propose a new method, termed as Lip by Speech (LIBS), of which the goal is to strengthen lip reading by learning from speech recognizers.
Ranked #2 on
Lipreading
on CMLR
Recent Advances in Diversified Recommendation
With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not.
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning
In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment.
Hierarchical Reinforcement Learning
reinforcement-learning
+4
