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Found 16 papers, 4 papers with code
DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation
Our research demonstrates the efficacy of leveraging AIGS and the DiffsFormer architecture to mitigate data scarcity in stock forecasting tasks.
Building Variable-sized Models via Learngene Pool
To overcome these challenges, motivated by the recently proposed Learngene framework, we propose a novel method called Learngene Pool.
Transformer as Linear Expansion of Learngene
Under the situation where we need to produce models of varying depths adapting for different resource constraints, TLEG achieves comparable results while reducing around 19x parameters stored to initialize these models and around 5x pre-training costs, in contrast to the pre-training and fine-tuning approach.
Manipulating the Label Space for In-Context Classification
Specifically, using our approach on ImageNet, we increase accuracy from 74. 70\% in a 4-shot setting to 76. 21\% with just 2 shots.
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning
FedDAT is the first approach that enables an efficient distributed finetuning of foundation models for a variety of heterogeneous Vision-Language tasks.
FedPop: Federated Population-based Hyperparameter Tuning
Similar to conventional ML pipelines, the client local optimization and server aggregation procedure in FL are sensitive to the hyperparameter (HP) selection.
Exploring Diverse In-Context Configurations for Image Captioning
After discovering that Language Models (LMs) can be good in-context few-shot learners, numerous strategies have been proposed to optimize in-context sequence configurations.
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering
Visual Question Answering (VQA) is a multi-discipline research task.
FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation
Real-world applications usually involve a distribution shift across the datasets of the different clients, which hurts the generalization ability of the clients to unseen samples from their respective data distributions.
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation
Then we study users' privacy decision making under different data disclosure mechanisms and recommendation models, and how their data disclosure decisions affect the recommender system's performance.
Towards Data-Free Domain Generalization
In particular, we address the question: How can knowledge contained in models trained on different source domains be merged into a single model that generalizes well to unseen target domains, in the absence of source and target domain data?
PURE: An Uncertainty-aware Recommendation Framework for Maximizing Expected Posterior Utility of Platform
In this paper, we propose a novel recommendation framework which effectively utilizes the information of user uncertainty over different item dimensions and explicitly takes into consideration the impact of display policy on user in order to achieve maximal expected posterior utility for the platform.
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences.
Towards Precise Intra-camera Supervised Person Re-identification
Our approach performs even comparable to state-of-the-art fully supervised methods in two of the datasets.
Large-scale Interactive Recommendation with Tree-structured Policy Gradient
Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance.
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards.
