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Found 56 papers, 20 papers with code
BudgetFusion: Perceptually-Guided Adaptive Diffusion Models
The observation motivates us to present BudgetFusion, a novel model that suggests the most perceptually efficient number of diffusion steps before a diffusion model starts to generate an image.
Group-wise normalization in differential abundance analysis of microbiome samples
A key challenge in differential abundance analysis of microbial samples is that the counts for each sample are compositional, resulting in biased comparisons of the absolute abundance across study groups.
Detect an Object At Once without Fine-tuning
When presented with one or a few photos of a previously unseen object, humans can instantly recognize it in different scenes.
An Evolved Universal Transformer Memory
Prior methods propose to offset the escalating costs of modern foundation models by dropping specific parts of their contexts with hand-designed rules, while attempting to preserve their original performance.
Low Latency Point Cloud Rendering with Learned Splatting
Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation.
Can-Do! A Dataset and Neuro-Symbolic Grounded Framework for Embodied Planning with Large Multimodal Models
Large multimodal models have demonstrated impressive problem-solving abilities in vision and language tasks, and have the potential to encode extensive world knowledge.
Improving Whisper's Recognition Performance for Under-Represented Language Kazakh Leveraging Unpaired Speech and Text
It is worth researching how to utilize low-cost data to improve the performance of Whisper on under-represented languages.
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning
Our approach, called Random Annealing Jump Start (RAJS), is tailored for real-world goal-oriented problems by leveraging prior feedback controllers as guide policy to facilitate environmental exploration and policy learning in RL.
FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries
Leveraging the power of LMMs, we introduce FabGPT, a customized IC fabrication large multimodal model for wafer defect knowledge query.
Transformer Layers as Painters
We further show that some classes of problems have robustness to skipping layers, running the layers in an order different from how they were trained, or running the layers in parallel.
Forest2Seq: Revitalizing Order Prior for Sequential Indoor Scene Synthesis
Synthesizing realistic 3D indoor scenes is a challenging task that traditionally relies on manual arrangement and annotation by expert designers.
BVI-RLV: A Fully Registered Dataset and Benchmarks for Low-Light Video Enhancement
Low-light videos often exhibit spatiotemporal incoherent noise, compromising visibility and performance in computer vision applications.
Image-GS: Content-Adaptive Image Representation via 2D Gaussians
Neural image representations have recently emerged as a promising technique for storing, streaming, and rendering visual data.
EG4D: Explicit Generation of 4D Object without Score Distillation
In recent years, the increasing demand for dynamic 3D assets in design and gaming applications has given rise to powerful generative pipelines capable of synthesizing high-quality 4D objects.
Defending Spiking Neural Networks against Adversarial Attacks through Image Purification
Spiking Neural Networks (SNNs) aim to bridge the gap between neuroscience and machine learning by emulating the structure of the human nervous system.
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility.
Configurable Learned Holography
Although the emerging learned approaches have enabled rapid and high-quality hologram generation, any alteration in display hardware still requires a retraining of the model.
Evolutionary Optimization of Model Merging Recipes
Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with significantly more parameters, despite not being explicitly trained for such tasks.
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs
Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs.
Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction
In this paper, we propose a Zero-shot Document-level Relation Triplet Extraction (ZeroDocRTE) framework, which generates labeled data by retrieval and denoising knowledge from LLMs, called GenRDK.
Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding
In this study, we tackle the challenge of classifying the object category in point clouds, which previous works like PointCLIP struggle to address due to the inherent limitations of the CLIP architecture.
MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
Toward Optimized VR/AR Ergonomics: Modeling and Predicting User Neck Muscle Contraction
Leveraging electromyography devices, we measure, model, and predict VR users' neck muscle contraction levels (MCL) while they move their heads to interact with the virtual environment.
S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields
Recently, Neural Radiance Field (NeRF) has shown great success in rendering novel-view images of a given scene by learning an implicit representation with only posed RGB images.
Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction
Therefore, how to select effective pseudo labels to denoise DS data is still a challenge in document-level distant relation extraction.
AutoColor: Learned Light Power Control for Multi-Color Holograms
For this purpose, we establish the first multi-color hologram dataset using synthetic images and their depth information.
A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU
Recent years have witnessed impressive progress in super-resolution (SR) processing.
Predicting crash injury severity in smart cities: a novel computational approach with wide and deep learning model
Smart cities came out as highly knowledgeable bio-networks, offering intelligent services and innovative solutions to urban problems.
Underwater target detection based on improved YOLOv7
Underwater target detection is a crucial aspect of ocean exploration.
Recurrent Generic Contour-based Instance Segmentation with Progressive Learning
It maintains a single estimate of the contour that is progressively deformed toward the object boundary.
Ranked #1 on
Semantic Contour Prediction
on Sbd val
Dirichlet-Neumann learning algorithm for solving elliptic interface problems
Non-overlapping domain decomposition methods are natural for solving interface problems arising from various disciplines, however, the numerical simulation requires technical analysis and is often available only with the use of high-quality grids, thereby impeding their use in more complicated situations.
Force-Aware Interface via Electromyography for Natural VR/AR Interaction
Closing the gap between real-world physicality and immersive virtual experience requires a closed interaction loop: applying user-exerted physical forces to the virtual environment and generating haptic sensations back to the users.
A Chit-Chats Enhanced Task-Oriented Dialogue Corpora for Fuse-Motive Conversation Systems
The goal of building intelligent dialogue systems has largely been separately pursued under two motives: task-oriented dialogue (TOD) systems, and open-domain systems for chit-chat (CC).
PCL: Proxy-Based Contrastive Learning for Domain Generalization
Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains.
Ranked #23 on
Domain Generalization
on PACS
Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks
Gradient-based methods for the distributed training of residual networks (ResNets) typically require a forward pass of the input data, followed by back-propagating the error gradient to update model parameters, which becomes time-consuming as the network goes deeper.
Layer-Parallel Training of Residual Networks with Auxiliary Variables
Backpropagation algorithm is indispensable for training modern residual networks (ResNets) and usually tends to be time-consuming due to its inherent algorithmic lockings.
Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview
Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route.
FoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality
To tackle these problems toward six-degrees-of-freedom, egocentric, and stereo NeRF in VR, we present the first gaze-contingent 3D neural representation and view synthesis method.
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
In this paper, we present an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles, which decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically.
Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm
In this paper, we propose an RL-based end-to-end decision-making method under a framework of offline training and online correction, called the Shielded Distributional Soft Actor-critic (SDSAC).
Recurrent Model Predictive Control
This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems.
Steadily Learn to Drive with Virtual Memory
Reinforcement learning has shown great potential in developing high-level autonomous driving.
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning
Firstly, a deep Gaussian process (DGP) model is built on the historical data to learn empirical knowledge.
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation
Emotion Recognition in Conversations (ERC) is essential for building empathetic human-machine systems.
A Practical Layer-Parallel Training Algorithm for Residual Networks
Gradient-based algorithms for training ResNets typically require a forward pass of the input data, followed by back-propagating the objective gradient to update parameters, which are time-consuming for deep ResNets.
Deep Multi Depth Panoramas for View Synthesis
We propose a learning-based approach for novel view synthesis for multi-camera 360$^{\circ}$ panorama capture rigs.
Mixed Reinforcement Learning with Additive Stochastic Uncertainty
This paper presents a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy with the purpose of improving both learning accuracy and training speed.
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic
In this paper, we introduce the minimax formulation and distributional framework to improve the generalization ability of RL algorithms and develop the Minimax Distributional Soft Actor-Critic (Minimax DSAC) algorithm.
Direct and indirect reinforcement learning
Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks.
Adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints
CADP linearizes the constrained optimization problem locally into a quadratically constrained linear programming problem, and then obtains the optimal update of the policy network by solving its dual problem.
DiffTaichi: Differentiable Programming for Physical Simulation
We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image
In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities.
Stochastic Training of Residual Networks: a Differential Equation Viewpoint
During the last few years, significant attention has been paid to the stochastic training of artificial neural networks, which is known as an effective regularization approach that helps improve the generalization capability of trained models.
Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling
Nonlocal neural networks have been proposed and shown to be effective in several computer vision tasks, where the nonlocal operations can directly capture long-range dependencies in the feature space.
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment
In this document we present an end-to-end machine reading comprehension system that solves multiple choice questions with a textual entailment perspective.
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices.
