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Found 17 papers, 8 papers with code
Textomics: A Dataset for Genomics Data Summary Generation
We further illustrate how Textomics can be used to advance other applications, including evaluating scientific paper embeddings and generating masked templates for scientific paper understanding.
Enhancing LLM Safety via Constrained Direct Preference Optimization
Empirically, our approach provides a safety guarantee to LLMs that is missing in DPO while achieving significantly higher rewards under the same safety constraint compared to a recently proposed safe RLHF approach.
T-Rex: Text-assisted Retrosynthesis Prediction
As a fundamental task in computational chemistry, retrosynthesis prediction aims to identify a set of reactants to synthesize a target molecule.
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning (VRL).
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score-Softmax Classifier
Deep neural networks enable real-time monitoring of in-vehicle driver, facilitating the timely prediction of distractions, fatigue, and potential hazards.
ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding
ForeSeer transfers reviews from similar products on a large product graph and exploits these reviews to predict aspects that might emerge in future reviews.
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
In this paper, we address the limitation above by 1) introducing vision-language Model with Multi-Modal In-Context Learning(MMICL), a new approach to allow the VLM to deal with multi-modal inputs efficiently; 2) proposing a novel context scheme to augment the in-context learning ability of the VLM; 3) constructing the Multi-modal In-Context Learning (MIC) dataset, designed to enhance the VLM's ability to understand complex multi-modal prompts.
Ranked #16 on
Visual Reasoning
on Winoground
DFWLayer: Differentiable Frank-Wolfe Optimization Layer
Differentiable optimization has received a significant amount of attention due to its foundational role in the domain of machine learning based on neural networks.
Over-training with Mixup May Hurt Generalization
Mixup, which creates synthetic training instances by linearly interpolating random sample pairs, is a simple and yet effective regularization technique to boost the performance of deep models trained with SGD.
Gradient-based Bi-level Optimization for Deep Learning: A Survey
In this survey, we first give a formal definition of the gradient-based bi-level optimization.
Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease
On top of BAR, we propose using a soft-label-capable supervised contrastive loss, aiming to learn the relative similarity of representations that reflect how mixed are the synthetic MRIs using our soft labels.
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning
Labels are costly and sometimes unreliable.
Ranked #5 on
Image Classification
on mini WebVision 1.0
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering
Images are more than a collection of objects or attributes -- they represent a web of relationships among interconnected objects.
Ranked #1 on
Graph Question Answering
on GQA
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies.
Quantum operations with indefinite time direction
A fundamental question is whether it is possible to conceive a broader set of operations that probe quantum processes in the backward direction, from the future to the past, or more generally, in a combination of the forward and backward directions.
Quantum Physics Mathematical Physics Mathematical Physics
Longitudinal Self-Supervised Learning
Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a large number of ground-truth labels to be informative.
DAWSON: A Domain Adaptive Few Shot Generation Framework
Based on DAWSON, We also propose MUSIC MATINEE, which is the first few-shot music generation model.
