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Found 33 papers, 7 papers with code
Are Expressive Models Truly Necessary for Offline RL?
Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL problem as a sequential modeling task, therefore bypassing the notoriously difficult credit assignment challenge of value learning in conventional RL paradigm.
A Riemannian Approach for Spatiotemporal Analysis and Generation of 4D Tree-shaped Structures
In this paper, we propose a novel mathematical representation of the shape space of such trajectories, a Riemannian metric on that space, and computational tools for fast and accurate spatiotemporal registration and geodesics computation between 4D tree-shaped structures.
Crafting Efficient Fine-Tuning Strategies for Large Language Models
This paper addresses the challenges of efficiently fine-tuning large language models (LLMs) by exploring data efficiency and hyperparameter optimization.
OED: Towards One-stage End-to-End Dynamic Scene Graph Generation
Dynamic Scene Graph Generation (DSGG) focuses on identifying visual relationships within the spatial-temporal domain of videos.
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation
To address these problems, we present a pipeline that integrates the conditional diffusion model for lymph node generation and the nnU-Net model for lymph node segmentation to improve the segmentation performance of abdominal lymph nodes through synthesizing a diversity of realistic abdominal lymph node data.
Detecting misinformation through Framing Theory: the Frame Element-based Model
In this paper, we delve into the rapidly evolving challenge of misinformation detection, with a specific focus on the nuanced manipulation of narrative frames - an under-explored area within the AI community.
Training A Multi-stage Deep Classifier with Feedback Signals
Multi-Stage Classifier (MSC) - several classifiers working sequentially in an arranged order and classification decision is partially made at each step - is widely used in industrial applications for various resource limitation reasons.
OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
Specifically, we consider the general SFT training data, consisting of a small amount of expert data mixed with a large proportion of sub-optimal data, without any preference labels.
Ranked #79 on
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AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization
In this paper, we propose an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews, which effectively captures the adaptive nature required for opinion summarization.
Instance-Variant Loss with Gaussian RBF Kernel for 3D Cross-modal Retriveal
Hence, the performance of cross-modal retrieval methods heavily depends on the representational capacity of this embedding space.
KATSum: Knowledge-aware Abstractive Text Summarization
The results reveal that the proposed framework can effectively utilise the information from Knowledge Graph and significantly reduce the factual errors in the summary.
PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection
3D object detection is a critical task in autonomous driving.
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing
In order to handle new entities in product titles and address the special language styles problem of product titles in e-commerce domain, we propose our textual entailment model with continuous prompt tuning based hypotheses and fusion embeddings for e-commerce entity typing.
Learning Consumer Preferences from Bundle Sales Data
In this paper, we propose an approach to learn the distribution of consumers' valuations toward the products using bundle sales data.
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system
Anomaly detection has been an active research area with a wide range of potential applications.
Transfer Learning for Retinal Vascular Disease Detection: A Pilot Study with Diabetic Retinopathy and Retinopathy of Prematurity
Recently, deep learning techniques have been successfully applied for detection of diabetic retinopathy (DR).
Fast Subspace Identification Method Based on Containerised Cloud Workflow Processing System
Subspace identification (SID) has been widely used in system identification and control fields since it can estimate system models only relying on the input and output data by reliable numerical operations such as singular value decomposition (SVD).
Comparison between Time Shifting Deviation and Cross-correlation Methods
It stands in clear contrast to the result of cross-correlation method, whose localization error is 70 m and the standard deviation is 208. 4 m. Compared with cross-correlation method, TSDEV has the same resistance to white noise, but has fewer boundary conditions and better suppression on linear drift or common noise, which leads to more precise TDE results.
A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
Heated debates continue over the best autonomous driving framework.
Elastic Shape Analysis of Tree-like 3D Objects using Extended SRVF Representation
We demonstrate the utility of this framework in comparing, matching, and computing geodesics between biological objects such as neurons and botanical trees.
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization
Matrix multiplication is the bedrock in Deep Learning inference application.
Graph2Graph Learning with Conditional Autoregressive Models
For such tasks, the main requirement for intermediate representations of the data is to maintain the structure needed for output, i. e., keeping classes separated or maintaining the order indicated by the regressor.
Truncation-Free Matching System for Display Advertising at Alibaba
When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds.
Adaptive N-step Bootstrapping with Off-policy Data
In this work, we point out that the optimal value of n actually differs on each data point, while the fixed value n is a rough average of them.
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their target users.
DCAF: A Dynamic Computation Allocation Framework for Online Serving System
These stages usually allocate resource manually with specific computing power budgets, which requires the serving configuration to adapt accordingly.
Early Detection of Retinopathy of Prematurity (ROP) in Retinal Fundus Images Via Convolutional Neural Networks
Retinopathy of prematurity (ROP) is an abnormal blood vessel development in the retina of a prematurely-born infant or an infant with low birth weight.
Kalibre: Knowledge-based Neural Surrogate Model Calibration for Data Center Digital Twins
However, in the context of CFD, each search step requires long-lasting CFD model's iterated solving, rendering these approaches impractical with increased model complexity.
Measure Contribution of Participants in Federated Learning
Our methods open the door for research in model contribution and credit allocation in the context of federated machine learning.
EnforceNet: Monocular Camera Localization in Large Scale Indoor Sparse LiDAR Point Cloud
Pose estimation is a fundamental building block for robotic applications such as autonomous vehicles, UAV, and large scale augmented reality.
Cooperative Lane Changing via Deep Reinforcement Learning
In this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning.
Interpret Federated Learning with Shapley Values
For host party to interpret a single prediction of vertical Federated Learning model, the interpretation results, namely the feature importance, are very likely to reveal the protected data from guest party.
Interactive Learning from Policy-Dependent Human Feedback
This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback.
