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Found 16 papers, 6 papers with code
Systematic Analysis of LLM Contributions to Planning: Solver, Verifier, Heuristic
In this work, we provide a systematic analysis of how large language models (LLMs) contribute to solving planning problems.
Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction
In this paper, we present a novel trajectory prediction model for autonomous driving, combining a Characterized Diffusion Module and a Spatial-Temporal Interaction Network to address the challenges posed by dynamic and heterogeneous traffic environments.
LASP: Surveying the State-of-the-Art in Large Language Model-Assisted AI Planning
Effective planning is essential for the success of any task, from organizing a vacation to routing autonomous vehicles and developing corporate strategies.
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding
The trained policy can subsequently be deployed for further data collection, resulting in an iterative training framework, which we refer to as iterative offline RL.
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation
This method trains the model to prioritize the best responses from a pool of candidates created for a particular task.
Pedestrian Recognition with Radar Data-Enhanced Deep Learning Approach Based on Micro-Doppler Signatures
As a hot topic in recent years, the ability of pedestrians identification based on radar micro-Doppler signatures is limited by the lack of adequate training data.
Moccasin: Efficient Tensor Rematerialization for Neural Networks
The deployment and training of neural networks on edge computing devices pose many challenges.
Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint
To tackle the challenge, we introduce an intermediate variable for grasp contact areas to constrain the grasp generation; in other words, we factorize the mapping into two sequential stages by assuming that grasping poses are fully constrained given contact maps: 1) we first learn contact map distributions to generate the potential contact maps for grasps; 2) then learn a mapping from the contact maps to the grasping poses.
Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis
Our main contributions include: 1) we present the first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features; 2) to enhance structural details of generated images, we propose to introduce auxiliary sketch guidance into a conditional GAN.
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
The delayed feedback problem is one of the imperative challenges in online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few minutes to several days.
GuideBoot: Guided Bootstrap for Deep Contextual Bandits
The proposed method is efficient as it can make decisions on-the-fly by utilizing only one randomly chosen model, but is also effective as we show that it can be viewed as a non-Bayesian approximation of Thompson sampling.
Classification with Strategically Withheld Data
To this end, we present {\sc IC-LR}, a modification of Logistic Regression that removes the incentive to strategically drop features.
Contrastive Rendering for Ultrasound Image Segmentation
However, the lack of sharp boundaries in US images still remains an inherent challenge for segmentation.
Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANs
Efficiently synthesizing realistic, editable and high resolution US images can solve the problems.
Minimizing Time-to-Rank: A Learning and Recommendation Approach
Consider the following problem faced by an online voting platform: A user is provided with a list of alternatives, and is asked to rank them in order of preference using only drag-and-drop operations.
A Cost-Effective Framework for Preference Elicitation and Aggregation
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features.
