Search Results for author:
Found 16 papers, 2 papers with code
Scalable Indoor Novel-View Synthesis using Drone-Captured 360 Imagery with 3D Gaussian Splatting
Prior methods have utilized drones for data capture and radiance fields for scene reconstruction, both of which present certain challenges.
Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective
In contrast, the Warmup-Stable-Decay (WSD) schedule uses a constant learning rate to produce a main branch of iterates that can in principle continue indefinitely without a pre-specified compute budget.
Hardness of Learning Neural Networks under the Manifold Hypothesis
In this paper, we investigate the hardness of learning under the manifold hypothesis.
Pandora's White-Box: Precise Training Data Detection and Extraction in Large Language Models
In fine-tuning, we find that a simple attack based on the ratio of the loss between the base and fine-tuned models is able to achieve near-perfect MIA performance; we then leverage our MIA to extract a large fraction of the fine-tuning dataset from fine-tuned Pythia and Llama models.
Closed Drafting as a Case Study for First-Principle Interpretability, Memory, and Generalizability in Deep Reinforcement Learning
Closed drafting or "pick and pass" is a popular game mechanic where each round players select a card or other playable element from their hand and pass the rest to the next player.
MoPe: Model Perturbation-based Privacy Attacks on Language Models
In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training data of a pre-trained language model, given white-box access to the models parameters.
The Naughtyformer: A Transformer Understands Offensive Humor
Jokes are intentionally written to be funny, but not all jokes are created the same.
Detecting Owner-member Relationship with Graph Convolution Network in Fisheye Camera System
The owner-member relationship between wheels and vehicles contributes significantly to the 3D perception of vehicles, especially in embedded environments.
EVOQUER: Enhancing Temporal Grounding with Video-Pivoted BackQuery Generation
Given a query and an untrimmed video, the temporal grounding model predicts the target interval, and the predicted video clip is fed into a video translation task by generating a simplified version of the input query.
Hierarchical Topic Presence Models
Motivated by a data set of web pages (documents) nested in web sites, we extend the Poisson factor analysis topic model to hierarchical topic presence models for analyzing text from documents nested in known groups.
Local and Global Topics in Text Modeling of Web Pages Nested in Web Sites
For web pages nested inside web sites, local topic models explicitly label local topics and identifies the owning web site.
DeepWORD: A GCN-based Approach for Owner-Member Relationship Detection in Autonomous Driving
It's worth noting that the owner-member relationship between wheels and vehicles has an significant contribution to the 3D perception of vehicles, especially in the embedded environment.
DiskFM: A Forward Modeling Tool for Disk Analysis with Coronagraphic Instruments
However, forward-modeling with disks is complicated by the fact that the disk cannot be simplified using a simple point source convolved by the PSF as the astrophysical model; all hypothetical disk morphologies must be explored to understand the subtle and non-linear effects of the PSF subtraction algorithm on the shape and local geometry of these systems.
Instrumentation and Methods for Astrophysics
Vehicle Re-ID for Surround-view Camera System
The vehicle re-identification (ReID) plays a critical role in the perception system of autonomous driving, which attracts more and more attention in recent years.
Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists.
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification.
