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Found 17 papers, 10 papers with code
PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer
Amidst the surge in deep learning-based password guessing models, challenges of generating high-quality passwords and reducing duplicate passwords persist.
Goldfish: An Efficient Federated Unlearning Framework
To address the challenge of low validity in existing machine unlearning algorithms, we propose a novel loss function.
CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models
Multimodal large language models (MLLMs) have demonstrated promising results in a variety of tasks that combine vision and language.
Model Composition for Multimodal Large Language Models
In this paper, we propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model.
Browse and Concentrate: Comprehending Multimodal Content via prior-LLM Context Fusion
With the bloom of Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive performance across diverse vision-language tasks.
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions
Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge not only in natural language tasks, but also in some vision-language tasks such as open-domain knowledge-based visual question answering (OK-VQA).
Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models
However, existing visual instruction tuning methods only utilize image-language instruction data to align the language and image modalities, lacking a more fine-grained cross-modal alignment.
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.
Weakly Supervised Vision-and-Language Pre-training with Relative Representations
Weakly supervised vision-and-language pre-training (WVLP), which learns cross-modal representations with limited cross-modal supervision, has been shown to effectively reduce the data cost of pre-training while maintaining decent performance on downstream tasks.
CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloud
At present, the anchor-based or anchor-free models that use LiDAR point clouds for 3D object detection use the center assigner strategy to infer the 3D bounding boxes.
Efficient DNA-driven nanocavities for approaching quasi-deterministic strong coupling to a few fluorophores
Strong coupling between light and matter is the foundation of promising quantum photonic devices such as deterministic single photon sources, single atom lasers and photonic quantum gates, which consist of an atom and a photonic cavity.
Optics Quantum Physics
AtomSets -- A Hierarchical Transfer Learning Framework for Small and Large Materials Datasets
Here we leverage the transfer learning concept and the graph network deep learning framework and develop the AtomSets machine learning framework for consistent high model accuracy at both small and large materials data.
Feature Engineering
Transfer Learning
Materials Science
Mask-Align: Self-Supervised Neural Word Alignment
Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks.
Holistic Combination of Structural and Textual Code Information for Context based API Recommendation
APIRec-CST is a deep learning model that combines the API usage with the text information in the source code based on an API Context Graph Network and a Code Token Network that simultaneously learn structural and textual features for API recommendation.
Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data
Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science.
Materials Science Disordered Systems and Neural Networks
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Similarly, we show that MEGNet models trained on $\sim 60, 000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.
Ranked #4 on
Formation Energy
on Materials Project
Drug Discovery
Formation Energy
Materials Science
Computational Physics
Automated Generation and Ensemble-Learned Matching of X-ray Absorption Spectra
We report the development of XASdb, a large database of computed reference X-ray absorption spectra (XAS), and a novel Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra.
Materials Science
