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Found 46 papers, 22 papers with code
Equilibrium Distribution for t-Distributed Stochastic Neighbor Embedding with Generalized Kernels
T-distributed stochastic neighbor embedding (t-SNE) is a well-known algorithm for visualizing high-dimensional data by finding low-dimensional representations.
Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models
The recent explosion of large language models (LLMs), each with its own general or specialized strengths, makes scalable, reliable benchmarking more urgent than ever.
Few-Step Diffusion via Score identity Distillation
Comprehensive experiments on SD1. 5 and SDXL demonstrate state-of-the-art performance in both one-step and few-step generation settings, along with robustness to the absence of real images.
LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch
We detail the training of the LLM360 K2-65B model, scaling up our 360-degree OPEN SOURCE approach to the largest and most powerful models under project LLM360.
Sharpening Your Density Fields: Spiking Neuron Aided Fast Geometry Learning
Neural Radiance Fields (NeRF) have achieved remarkable progress in neural rendering.
Discover physical concepts and equations with machine learning
Machine learning can uncover physical concepts or physical equations when prior knowledge from the other is available.
AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer
Consequently, the combination of a family aware Transformer network and an expansive dataset enables AniMer to outperform existing methods not only on 3D datasets like Animal3D and CtrlAni3D, but also on out-of-distribution Animal Kingdom dataset.
Robust SG-NeRF: Robust Scene Graph Aided Neural Surface Reconstruction
We also devise a scene graph updating strategy to provide more accurate information throughout the training process.
Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step
SiDA utilizes the encoder from the generator's score network as a discriminator, allowing it to distinguish between real images and those generated by SiD.
Ranked #1 on
Image Generation
on FFHQ 64x64
ChatHouseDiffusion: Prompt-Guided Generation and Editing of Floor Plans
The generation and editing of floor plans are critical in architectural planning, requiring a high degree of flexibility and efficiency.
Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detection
To tackle these issues, we propose D2UE, a Diversified Dual-space Uncertainty Estimation framework for medical anomaly detection.
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation
3D reconstruction from a single radiograph, so-called 2D-3D reconstruction, offers the possibility of various clinical applications, but achieving clinically viable accuracy and computational efficiency is still an unsolved challenge.
Speech Recognition Rescoring with Large Speech-Text Foundation Models
Large language models (LLM) have demonstrated the ability to understand human language by leveraging large amount of text data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Image
Unlike in visual analysis, pixel values in quantitative images correspond to physical metrics measured by specific devices (e. g., a depth image).
Text Grafting: Near-Distribution Weak Supervision for Minority Classes in Text Classification
Specifically, we first use LLM-based logits to mine masked templates from the raw corpus, which have a high potential for data synthesis into the target minority class.
Pandora: Towards General World Model with Natural Language Actions and Video States
This paper makes a step towards building a general world model by introducing Pandora, a hybrid autoregressive-diffusion model that simulates world states by generating videos and allows real-time control with free-text actions.
Diffusion-RPO: Aligning Diffusion Models through Relative Preference Optimization
Aligning large language models with human preferences has emerged as a critical focus in language modeling research.
LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
(2) We develop LLM Reasoners, a library for standardized modular implementation of existing and new reasoning algorithms, under a unified formulation of the search, reward, and world model components.
Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts
In the field of large language models (LLMs), aligning models with the diverse preferences of users is a critical challenge.
INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs.
Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs
However, as the classification is subjective, we aimed to develop an automated approach to classify the disease severity based on the two grades using digitally-reconstructed radiographs (DRRs) from CT images.
LLM360: Towards Fully Transparent Open-Source LLMs
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
Deep Reinforcement Learning-Based Battery Conditioning Hierarchical V2G Coordination for Multi-Stakeholder Benefits
On the grid side, load fluctuations and renewable energy consumption are considered, while on the EVA side, energy constraints and charging costs are considered.
Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography
The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0. 880 and 0. 920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3. 27 to 3. 79% for four measurements with different poses.
MSKdeX: Musculoskeletal (MSK) decomposition from an X-ray image for fine-grained estimation of lean muscle mass and muscle volume
We propose a method (named MSKdeX) to estimate fine-grained muscle properties from a plain X-ray image, a low-cost, low-radiation, and highly accessible imaging modality, through musculoskeletal decomposition leveraging fine-grained segmentation in CT. We train a multi-channel quantitative image translation model to decompose an X-ray image into projections of CT of individual muscles to infer the lean muscle mass and muscle volume.
Reasoning with Language Model is Planning with World Model
RAP on LLAMA-33B surpasses CoT on GPT-4 with 33% relative improvement in a plan generation setting.
Why semantics matters: A deep study on semantic particle-filtering localization in a LiDAR semantic pole-map
Specifically, semantic segmentation is achieved by a new mask-range transformer network in a mask-classfication paradigm.
Language Models Meet World Models: Embodied Experiences Enhance Language Models
While large language models (LMs) have shown remarkable capabilities across numerous tasks, they often struggle with simple reasoning and planning in physical environments, such as understanding object permanence or planning household activities.
Learning Markov Random Fields for Combinatorial Structures via Sampling through Lovász Local Lemma
Contrastive divergence is then applied to separate these samples from those in the training set.
JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions
Hence, in order to achieve higher performance on our tasks, models need to effectively utilize such functional knowledge to infer the outcomes of actions, rather than relying solely on memorizing facts.
Revisiting the Roles of "Text" in Text Games
Text games present opportunities for natural language understanding (NLU) methods to tackle reinforcement learning (RL) challenges.
BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learning
We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image.
Finding Fallen Objects Via Asynchronous Audio-Visual Integration
The way an object looks and sounds provide complementary reflections of its physical properties.
MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentation
To answer this question, we propose a unified mask-classification model, MaskRange, for the range-view based LiDAR semantic and panoptic segmentation.
Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization
A greedy initialization-based resampling particle swarm optimization (GI-RPSO) algorithm is proposed to solve the model.
Learning Moving-Object Tracking with FMCW LiDAR
In this paper, we propose a learning-based moving-object tracking method utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR.
Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning
In self-supervised learning frameworks, deep networks are optimized to align different views of an instance that contains the similar visual semantic information.
Degree Counting Theorems for 2x2 non-symmetric singular Liouville Systems
Let $(M, g)$ be a compact Riemann surface with no boundary and $u=(u_1, u_2)$ be a solution of the following singular Liouville system: $$\Delta_g u_i+\sum_{j=1}^2 a_{ij}\rho_j(\frac{h_je^{u_j}}{\int_M h_je^{u_j}dV_g}-1)=\sum_{l=1}^{N}4\pi\gamma_l(\delta_{p_l}-1), $$ where $h_1, h_2$ are positive smooth functions, $p_1,\cdots, p_N$ are distinct points on $M$, $\delta_{p_l}$ are Dirac masses, $\rho=(\rho_1,\rho_2)(\rho_i\geq 0)$ and $(\gamma_1,\cdots,\gamma_N)(\gamma_l > -1)$ are constant vectors.
Analysis of PDEs 35A01, 35B44, 35B45
Association: Remind Your GAN not to Forget
Neural networks are susceptible to catastrophic forgetting.
Generative and Discriminative Learning for Distorted Image Restoration
Due to the uncertainty in the distortion variation, restoring distorted images caused by liquify filter is a challenging task.
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics
We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors.
Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty
We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit.
A novel image tag completion method based on convolutional neural network
In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN).
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously.
Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function
To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure.
