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Found 43 papers, 14 papers with code
Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs
Multi-view understanding, the ability to reconcile visual information across diverse viewpoints for effective navigation, manipulation, and 3D scene comprehension, is a fundamental challenge in Multi-Modal Large Language Models (MLLMs) to be used as embodied agents.
CellFlow: Simulating Cellular Morphology Changes via Flow Matching
Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology.
Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review
We review these methods from a unified perspective, demonstrating that current techniques -- such as Sequential Monte Carlo (SMC)-based guidance, value-based sampling, and classifier guidance -- aim to approximate soft optimal denoising processes (a. k. a.
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation
The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation.
Predicting Human Brain States with Transformer
The human brain is a complex and highly dynamic system, and our current knowledge of its functional mechanism is still very limited.
Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation
Radiology report generation (RRG) has shown great potential in assisting radiologists by automating the labor-intensive task of report writing.
CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLM
This paper aims to design a unified Computer-Aided Design (CAD) generation system that can easily generate CAD models based on the user's inputs in the form of textual description, images, point clouds, or even a combination of them.
An Information Criterion for Controlled Disentanglement of Multimodal Data
Multimodal representation learning seeks to relate and decompose information inherent in multiple modalities.
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design
Finally, we demonstrate the effectiveness of DRAKES in generating DNA and protein sequences that optimize enhancer activity and protein stability, respectively, important tasks for gene therapies and protein-based therapeutics.
"What" x "When" working memory representations using Laplace Neural Manifolds
We study a specific choice $\unicode{x2013}$ a Laplace space with exponential basis functions for time coupled to an "Inverse Laplace" space with circumscribed basis functions in time.
Unsupervised dMRI Artifact Detection via Angular Resolution Enhancement and Cycle Consistency Learning
UdAD-AC leverages dMRI angular resolution enhancement and cycle consistency learning to capture the effective representation of artifact-free dMRI data during training, and it identifies data containing artifacts using designed confidence score during inference.
Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Latent Generation
Our key discovery is that coarse-grained noises in earlier denoising steps have demonstrated high motion consistency across consecutive video frames.
Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging
Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules, offering the capability to inspect tissue microstructures and is the only in-vivo method to reconstruct white matter fiber tracts non-invasively.
Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding
Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences.
LADDER: Language Driven Slice Discovery and Error Rectification
Error slice discovery is crucial to diagnose and mitigate model errors.
Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysis
The heterogeneity of neurological conditions, ranging from structural anomalies to functional impairments, presents a significant challenge in medical imaging analysis tasks.
Evaluating Zero-Shot Long-Context LLM Compression
This study evaluates the effectiveness of zero-shot compression techniques on large language models (LLMs) under long-context.
In-Context Symmetries: Self-Supervised Learning through Contextual World Models
In this work, drawing insights from world models, we propose to instead learn a general representation that can adapt to be invariant or equivariant to different transformations by paying attention to context -- a memory module that tracks task-specific states, actions, and future states.
How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks
This paper focuses on an early stage phase of deep learning research, prior to model development, and proposes a strategic framework for estimating the amount of annotated data required to train patch-based segmentation networks.
Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling
Reasoning from sequences of raw sensory data is a ubiquitous problem across fields ranging from medical devices to robotics.
Dirichlet Flow Matching with Applications to DNA Sequence Design
Further, we provide distilled Dirichlet flow matching, which enables one-step sequence generation with minimal performance hits, resulting in $O(L)$ speedups compared to autoregressive models.
MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning
Recently, the astonishing performance of large language models (LLMs) in natural language comprehension and generation tasks triggered lots of exploration of using them as central controllers to build agent systems.
Removing Biases from Molecular Representations via Information Maximization
High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug.
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome
On the hardware side, we modify the datapath of current PIM accelerators to accommodate epitomes and implement a feature map reuse technique to reduce computation cost.
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training
Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction.
A hybrid Decoder-DeepONet operator regression framework for unaligned observation data
Results illustrate the advantages of Decoder-DeepONet and Multi-Decoder-DeepONet in handling unaligned observation data and showcase their potentials in improving prediction accuracy.
Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI
However, due to the insufficient amount of precise annotations, thigh muscle masks generated by deep learning approaches tend to misclassify intra-muscular fat (IMF) as muscle impacting the analysis of muscle volumetrics.
Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation
An accurate and robust quantitative measurement of brain volume change is paramount for translational research and clinical applications.
Accurate Instance-Level CAD Model Retrieval in a Large-Scale Database
We present a new solution to the fine-grained retrieval of clean CAD models from a large-scale database in order to recover detailed object shape geometries for RGBD scans.
VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM
Event cameras have recently gained in popularity as they hold strong potential to complement regular cameras in situations of high dynamics or challenging illumination.
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning
In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.
Precise Indoor Positioning Based on UWB and Deep Learning
We examined UWB-based indoor location in conjunction with a fingerprint technique in this work.
DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation
To the best of our knowledge, we are the first to make a reasonable dynamic runtime scheduler on the combination of tensor swapping and tensor recomputation without user oversight.
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement
Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed great success in cross-domain computer vision tasks, enhancing the generalization ability of data-driven deep learning architectures by bridging the domain distribution gaps.
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting
Based on the homophily assumption of GNN, we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution.
Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications
Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.
Open Domain Generalization with Domain-Augmented Meta-Learning
Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain's annotated data are unavailable.
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs
Within the prosperity of Massive Open Online Courses (MOOCs), the education applications that automatically provide extracurricular knowledge for MOOC users become rising research topics.
MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs
The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e. g., course concept extraction, prerequisite relation discovery, etc.
Course Concept Expansion in MOOCs with External Knowledge and Interactive Game
As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users.
Multiple Sclerosis Lesion Inpainting Using Non-Local Partial Convolutions
However, existing methods inpaint lesions based on texture information derived from local surrounding tissue, often leading to inconsistent inpainting and the generation of artifacts such as intensity discrepancy and blurriness.
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning
In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space.
