Search Results for author:
Found 24 papers, 8 papers with code
Supervised Learning-enhanced Multi-Group Actor Critic for Live Stream Allocation in Feed
To address these challenges, we propose a novel Supervised Learning-enhanced Multi-Group Actor Critic algorithm (SL-MGAC).
MARM: Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity
Considering the above differences with LLM, we can draw a conclusion that: for a RecSys model, compared to model parameters, the computational complexity FLOPs is a more expensive factor that requires careful control.
GPT-4o System Card
We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.
Ranked #1 on
Visual Question Answering (VQA)
on VLM2-Bench
Enhancing Playback Performance in Video Recommender Systems with an On-Device Gating and Ranking Framework
Specifically, we utilize a gate model to identify videos that may have playback issues in real-time, and then we employ a ranking model to select the optimal result from a locally-cached pool to replace the stuttering videos.
RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems
Then, we propose a reinforcement prediction-allocation framework (RPAF) to address these issues.
Cache-Aware Reinforcement Learning in Large-Scale Recommender Systems
The recommendation with a cache is a solution to this problem, where a user-wise result cache is used to provide recommendations when the recommender system cannot afford a real-time computation.
Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated Topics
When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially significant contentious issues.
GOLF: Goal-Oriented Long-term liFe tasks supported by human-AI collaboration
By shifting the focus from short-term tasks to the broader spectrum of long-term life goals, this research underscores the transformative potential of LLMs in enhancing human decision-making processes and task management, marking a significant step forward in the evolution of human-AI collaboration.
Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement
In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios. Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions. Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed.
Task Supportive and Personalized Human-Large Language Model Interaction: A User Study
Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks.
Masked Pre-training Enables Universal Zero-shot Denoiser
In this work, we observe that model trained on vast general images via masking strategy, has been naturally embedded with their distribution knowledge, thus spontaneously attains the underlying potential for strong image denoising.
CREAD: A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems
These results highlight the effectiveness of the CREAD framework in watch time prediction in video recommender systems.
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution
In recent years, there has been a growing interest in utilizing reinforcement learning (RL) to optimize long-term rewards in recommender systems.
Multi-agent Reinforcement Learning
Recommendation Systems
+3
Stronger Fewer & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation
Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability we introduce a robust fine-tuning approach namely "Rein" to parameter-efficiently harness VFMs for DGSS.
Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation
Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely Rein, to parameter-efficiently harness VFMs for DGSS.
Ranked #1 on
Domain Adaptation
on GTAV to Cityscapes+Mapillary
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Ranked #1 on
Question Answering
on PeerQA
GPT-NeoX-20B: An Open-Source Autoregressive Language Model
We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a permissive license.
Ranked #52 on
Multi-task Language Understanding
on MMLU
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph
Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i. e., weights) of passenger demands between two connected regions.
Quantum-limited Localisation and Resolution in Three Dimensions
As a method to extract information from optical system, imaging can be viewed as a parameter estimation problem.
Quantum Physics Optics
LFTag: A Scalable Visual Fiducial System with Low Spatial Frequency
Visual fiducial systems are a key component of many robotics and AR/VR applications for 6-DOF monocular relative pose estimation and target identification.
Reverse Attention-Based Residual Network for Salient Object Detection
Benefiting from the quick development of deep convolutional neural networks, especially fully convolutional neural networks (FCNs), remarkable progresses have been achieved on salient object detection recently.
Active Image Synthesis for Efficient Labeling
The great success achieved by deep neural networks attracts increasing attention from the manufacturing and healthcare communities.
Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation
Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions.
Reverse Attention for Salient Object Detection
Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently.
