Search Results for author:
Found 13 papers, 7 papers with code
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM
Key-value (KV) caching has become the de-facto to accelerate generation speed for large language models (LLMs) inference.
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS).
UGG: Unified Generative Grasping
Generation-based methods that generate grasping postures conditioned on the object can often produce diverse grasping, but they are insufficient for high grasping success due to lack of discriminative information.
Token Prediction as Implicit Classification to Identify LLM-Generated Text
This paper introduces a novel approach for identifying the possible large language models (LLMs) involved in text generation.
Towards Sustainable Learning: Coresets for Data-efficient Deep Learning
To improve the efficiency and sustainability of learning deep models, we propose CREST, the first scalable framework with rigorous theoretical guarantees to identify the most valuable examples for training non-convex models, particularly deep networks.
GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
This paper presents a novel approach for detecting ChatGPT-generated vs. human-written text using language models.
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
The most popular and accessible 3D representation, i. e., point clouds, involves discrete samples of the underlying continuous 3D surface.
Ranked #5 on
3D Point Cloud Linear Classification
on ModelNet40
(using extra training data)
3D Point Cloud Classification
3D Point Cloud Linear Classification
+3
Semi-supervised Long-tailed Recognition using Alternate Sampling
A consistency loss has been introduced to limit the impact from unlabeled data while leveraging them to update the feature embedding.
Breadcrumbs: Adversarial Class-Balanced Sampling for Long-tailed Recognition
It is shown that, unlike class-balanced sampling, this is an adversarial augmentation strategy.
GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition
A new learning algorithm is then proposed for GeometrIc Structure Transfer (GIST), with resort to a combination of loss functions that combine class-balanced and random sampling to guarantee that, while overfitting to the popular classes is restricted to geometric parameters, it is leveraged to transfer class geometry from popular to few-shot classes.
Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas
By the proposed physical regularization, our method can generate HDRs which are not only visually appealing but also physically plausible.
Few-Shot Open-Set Recognition using Meta-Learning
It is argued that the classic softmax classifier is a poor solution for open-set recognition, since it tends to overfit on the training classes.
Understanding and Exploiting Object Interaction Landscapes
We introduce a new general representation for proximal interactions among physical objects that is agnostic to the type of objects or interaction involved.
