Search Results for author:
Found 11 papers, 4 papers with code
Automated Essay Scoring via Pairwise Contrastive Regression
To this end, in this paper we take inspiration from contrastive learning and propose a novel unified Neural Pairwise Contrastive Regression (NPCR) model in which both objectives are optimized simultaneously as a single loss.
Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Network
Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Peclet number.
UnifiedSSR: A Unified Framework of Sequential Search and Recommendation
In this work, we propose a Unified framework of Sequential Search and Recommendation (UnifiedSSR) for joint learning of user behavior history in both search and recommendation scenarios.
DarkVision: A Benchmark for Low-light Image/Video Perception
In this paper, we contribute the first multi-illuminance, multi-camera, and low-light dataset, named DarkVision, serving for both image enhancement and object detection.
Hierarchical Transformer with Spatio-Temporal Context Aggregation for Next Point-of-Interest Recommendation
The stacking of encoders captures the latent hierarchical structure of the check-in sequence, which is used to predict the next visiting POI.
Deep Causal Reasoning for Recommendations
As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation.
Cross-modal Variational Auto-encoder for Content-based Micro-video Background Music Recommendation
Moreover, the multimodal information is fused by the product-of-experts (PoE) principle, where the semantic information in visual and textual modalities of the micro-video are weighted according to their variance estimations such that the modality with a lower noise level is given more weights.
Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning
Reinforcement learning (RL) is an effective technique for training decision-making agents through interactions with their environment.
Online and Scalable Model Selection with Multi-Armed Bandits
Many online applications running on live traffic are powered by machine learning models, for which training, validation, and hyper-parameter tuning are conducted on historical data.
Dynamic Bidding Strategies with Multivariate Feedback Control for Multiple Goals in Display Advertising
This method generates a control score for each KPI, based on both the output of a PID controller module and a metric that quantifies the importance of each KPI for internal business needs.
Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework
As an emerging type of user-generated content, micro-video drastically enriches people's entertainment experiences and social interactions.
