Search Results for author:
Found 32 papers, 14 papers with code
Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis
On the other hand, AdSPT uses a novel domain adversarial training strategy to learn domain-invariant representations between each source domain and the target domain.
Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction
In this paper, we design a novel synchronous dual network (SDN) with cross-type attention via separately and interactively considering the entity types and relation types.
BioChemInsight: An Open-Source Toolkit for Automated Identification and Recognition of Optical Chemical Structures and Activity Data in Scientific Publications
Automated extraction of chemical structures and their bioactivity data is crucial for accelerating drug discovery and enabling data-driven pharmaceutical research.
Goku: Flow Based Video Generative Foundation Models
This paper introduces Goku, a state-of-the-art family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance.
Reliable and superior elliptic Fourier descriptor normalization and its application software ElliShape with efficient image processing
Based on these improvements, we developed ElliShape, a user-friendly software.
Structure Similarity Preservation Learning for Asymmetric Image Retrieval
The centroid vectors in the quantizer serve as anchor points in the embedding space of the gallery model to characterize its structure.
Asymmetric Feature Fusion for Image Retrieval
Then, a dynamic mixer is introduced to aggregate these features into compact embedding for efficient search.
StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides
Many tools exist for extracting structural and physiochemical descriptors from linear peptides to predict their properties, but similar tools for hydrocarbon-stapled peptides are lacking. Here, we present StaPep, a Python-based toolkit designed for generating 2D/3D structures and calculating 21 distinct features for hydrocarbon-stapled peptides. The current version supports hydrocarbon-stapled peptides containing 2 non-standard amino acids (norleucine and 2-aminoisobutyric acid) and 6 nonnatural anchoring residues (S3, S5, S8, R3, R5 and R8). Then we established a hand-curated dataset of 201 hydrocarbon-stapled peptides and 384 linear peptides with sequence information and experimental membrane permeability, to showcase StaPep's application in artificial intelligence projects. A machine learning-based predictor utilizing above calculated features was developed with AUC of 0. 85, for identifying cell-penetrating hydrocarbon-stapled peptides. StaPep's pipeline spans data retrieval, cleaning, structure generation, molecular feature calculation, and machine learning model construction for hydrocarbon-stapled peptides. The source codes and dataset are freely available on Github: https://github. com/dahuilangda/stapep_package.
CoT-Driven Framework for Short Text Classification: Enhancing and Transferring Capabilities from Large to Smaller Model
Consequently, this study first employs CoT to investigate and enhance the capabilities of LLMs in STC tasks.
Efficient LLM inference solution on Intel GPU
A customized Scaled-Dot-Product-Attention kernel is designed to match our fusion policy based on the segment KV cache solution.
HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images
MIB supplements the fixed sample grid in CNN of the conventional backbone network with tokens of different interaction ranges.
Efficient LDPC Decoding using Physical Computation
Due to 5G deployment, there is significant interest in LDPC decoding.
A 97 fJ/Conversion Neuron-ADC with Reconfigurable Sampling and Static Power Reduction
A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work.
Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief
Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases.
SimVQA: Exploring Simulated Environments for Visual Question Answering
By exploiting 3D and physics simulation platforms, we provide a pipeline to generate synthetic data to expand and replace type-specific questions and answers without risking the exposure of sensitive or personal data that might be present in real images.
Contextual Similarity Distillation for Asymmetric Image Retrieval
To this end, we propose a flexible contextual similarity distillation framework to enhance the small query model and keep its output feature compatible with that of large gallery model, which is crucial with asymmetric retrieval.
Learning Token-based Representation for Image Retrieval
Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention.
Ranked #3 on
Image Retrieval
on RParis (Medium)
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
Since our re-ranking model is not directly involved with the visual feature used in the initial retrieval, it is ready to be applied to retrieval result lists obtained from various retrieval algorithms.
Separating Skills and Concepts for Novel Visual Question Answering
Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models.
Learning Deep Local Features With Multiple Dynamic Attentions for Large-Scale Image Retrieval
To this end, we propose a novel deep local feature learning architecture to simultaneously focus on multiple discriminative local patterns in an image.
Learning from Lexical Perturbations for Consistent Visual Question Answering
Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations.
Large Scale Neural Architecture Search with Polyharmonic Splines
Neural Architecture Search (NAS) is a powerful tool to automatically design deep neural networks for many tasks, including image classification.
NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search
The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset.
Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning
Then we develop two algorithms for optimizing the energy efficiency of train operation.
A selected review on reinforcement learning based control for autonomous underwater vehicles
This paper provides a selected review on RL based control for AUVs with the focus on applications of RL to low-level control tasks for underwater regulation and tracking.
Robotics
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries
We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes.
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning
To tackle this problem, we propose a general acceleration method for model-free, off-policy deep RL algorithms by drawing the idea underlying regularized Anderson acceleration (RAA), which is an effective approach to accelerating the solving of fixed point problems with perturbations.
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
We provide a detailed analysis of the characteristics of the Fashion IQ data, and present a transformer-based user simulator and interactive image retriever that can seamlessly integrate visual attributes with image features, user feedback, and dialog history, leading to improved performance over the state of the art in dialog-based image retrieval.
Dialog-based Interactive Image Retrieval
Experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface.
A Systematic Analysis for State-of-the-Art 3D Lung Nodule Proposals Generation
Lung nodule proposals generation is the primary step of lung nodule detection and has received much attention in recent years .
S3Pool: Pooling with Stochastic Spatial Sampling
We view the pooling operation in CNNs as a two-step procedure: first, a pooling window (e. g., $2\times 2$) slides over the feature map with stride one which leaves the spatial resolution intact, and second, downsampling is performed by selecting one pixel from each non-overlapping pooling window in an often uniform and deterministic (e. g., top-left) manner.
Robust Regression on Image Manifolds for Ordered Label Denoising
In this paper, we present a computationally efficient and non-parametric method for robust regression on manifolds.
