arXiv ID
string | arXiv URL
string | PDF URL
string | DOI
string | Publication Date
timestamp[ns] | Updated Date
string | Title
string | Authors
string | Author Affiliations
string | Abstract
string | Categories
string | Primary Category
string | Comment
string | Journal Reference
string | Matched Conferences
string | label
int64 | source
string | classification_embedding
list | proximity_embedding
list | top_10_similar
list | max_similarity
float64 | avg_similarity
float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2301.01743v1
|
http://arxiv.org/abs/2301.01743v1
|
http://arxiv.org/pdf/2301.01743v1
| null | 2023-01-01T00:00:00
|
2023-01-01
|
Chatbots as Problem Solvers: Playing Twenty Questions with Role Reversals
|
David Noever; Forrest McKee
| null |
New chat AI applications like ChatGPT offer an advanced understanding of question context and memory across multi-step tasks, such that experiments can test its deductive reasoning. This paper proposes a multi-role and multi-step challenge, where ChatGPT plays the classic twenty-questions game but innovatively switches roles from the questioner to the answerer. The main empirical result establishes that this generation of chat applications can guess random object names in fewer than twenty questions (average, 12) and correctly guess 94% of the time across sixteen different experimental setups. The research introduces four novel cases where the chatbot fields the questions, asks the questions, both question-answer roles, and finally tries to guess appropriate contextual emotions. One task that humans typically fail but trained chat applications complete involves playing bilingual games of twenty questions (English answers to Spanish questions). Future variations address direct problem-solving using a similar inquisitive format to arrive at novel outcomes deductively, such as patentable inventions or combination thinking. Featured applications of this dialogue format include complex protein designs, neuroscience metadata, and child development educational materials.
|
cs.AI; cs.CL
|
cs.AI
| null | null | null | 0
|
ArXiv
|
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] |
[] | null | null |
2301.00330v2
|
http://arxiv.org/abs/2301.00330v2
|
http://arxiv.org/pdf/2301.00330v2
| null | 2023-01-01T00:00:00
|
2023-03-25
|
Efficient On-device Training via Gradient Filtering
|
Yuedong Yang; Guihong Li; Radu Marculescu
| null |
Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-propagation algorithm. Consequently, in this paper, we propose a new gradient filtering approach which enables on-device CNN model training. More precisely, our approach creates a special structure with fewer unique elements in the gradient map, thus significantly reducing the computational complexity and memory consumption of back propagation during training. Extensive experiments on image classification and semantic segmentation with multiple CNN models (e.g., MobileNet, DeepLabV3, UPerNet) and devices (e.g., Raspberry Pi and Jetson Nano) demonstrate the effectiveness and wide applicability of our approach. For example, compared to SOTA, we achieve up to 19$\times$ speedup and 77.1% memory savings on ImageNet classification with only 0.1% accuracy loss. Finally, our method is easy to implement and deploy; over 20$\times$ speedup and 90% energy savings have been observed compared to highly optimized baselines in MKLDNN and CUDNN on NVIDIA Jetson Nano. Consequently, our approach opens up a new direction of research with a huge potential for on-device training.
|
cs.CV; cs.AI; cs.LG
|
cs.CV
|
CVPR2023, 19 pages, 13 figures
| null |
CVPR
| 1
|
CVPR
|
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] |
[] | null | null |
2301.00409v1
|
http://arxiv.org/abs/2301.00409v1
|
http://arxiv.org/pdf/2301.00409v1
| null | 2023-01-01T00:00:00
|
2023-01-01
|
Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification
|
Shizhan Gong; Cheng Chen; Yuqi Gong; Nga Yan Chan; Wenao Ma; Calvin Hoi-Kwan Mak; Jill Abrigo; Qi Dou
| null |
Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance due to their dependence on specific landmarks or simplified anatomical assumptions. In this paper, we propose a novel semi-supervised framework to accurately measure the scale of MLS from head CT scans. We formulate the MLS measurement task as a deformation estimation problem and solve it using a few MLS slices with sparse labels. Meanwhile, with the help of diffusion models, we are able to use a great number of unlabeled MLS data and 2793 non-MLS cases for representation learning and regularization. The extracted representation reflects how the image is different from a non-MLS image and regularization serves an important role in the sparse-to-dense refinement of the deformation field. Our experiment on a real clinical brain hemorrhage dataset has achieved state-of-the-art performance and can generate interpretable deformation fields.
|
cs.CV; cs.AI
|
cs.CV
|
12 pages, 5 figures
| null | null | 0
|
ArXiv
|
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] |
[] | null | null |
2301.00383v2
|
http://arxiv.org/abs/2301.00383v2
|
http://arxiv.org/pdf/2301.00383v2
|
10.1109/TIP.2023.3235583
| 2023-01-01T00:00:00
|
2023-02-13
|
Discriminative Radial Domain Adaptation
|
Zenan Huang; Jun Wen; Siheng Chen; Linchao Zhu; Nenggan Zheng
| null |
Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDA) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.
|
cs.LG; cs.CV
|
cs.LG
|
13 pages, 14 figures
| null | null | 0
|
ArXiv
|
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] |
[] | null | null |
2301.00406v4
|
http://arxiv.org/abs/2301.00406v4
|
http://arxiv.org/pdf/2301.00406v4
| null | 2023-01-01T00:00:00
|
2024-03-06
|
Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data
|
Rui Ding; Juntian Ye; Qifeng Gao; Feihu Xu; Yuping Duan
| null |
Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly possibility to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.
|
cs.CV; eess.IV
|
cs.CV
| null | null | null | 0
|
ArXiv
|
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] |
[] | null | null |
2301.00452v2
|
http://arxiv.org/abs/2301.00452v2
|
http://arxiv.org/pdf/2301.00452v2
| null | 2023-01-01T00:00:00
|
2023-06-06
| "Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robo(...TRUNCATED)
|
Yonghao Long; Wang Wei; Tao Huang; Yuehao Wang; Qi Dou
| null | "Surgical robot automation has attracted increasing research interest over the past decade, expectin(...TRUNCATED)
|
cs.RO; cs.AI; cs.CV; cs.LG
|
cs.RO
| null | null | null | 0
|
ArXiv
| [-1.1542448997497559,0.49011972546577454,0.455150842666626,0.2676153779029846,-0.3660809397697449,0.(...TRUNCATED)
| [0.053660910576581955,0.7885090112686157,-0.5083746314048767,0.35763949155807495,0.07257240265607834(...TRUNCATED)
|
[] | null | null |
2301.00399v1
|
http://arxiv.org/abs/2301.00399v1
|
http://arxiv.org/pdf/2301.00399v1
| null | 2023-01-01T00:00:00
|
2023-01-01
|
Semantic Operator Prediction and Applications
|
Farshad Noravesh
| null | "In the present paper, semantic parsing challenges are briefly introduced and QDMR formalism in sema(...TRUNCATED)
|
cs.CL
|
cs.CL
| null | null | null | 0
|
ArXiv
| [-0.5906028747558594,0.5990965366363525,0.19114141166210175,-1.4667534828186035,0.6515888571739197,-(...TRUNCATED)
| [0.3160783350467682,0.8817468285560608,0.06231136620044708,-0.3230496346950531,-0.03929244354367256,(...TRUNCATED)
|
[] | null | null |
2301.00447v1
|
http://arxiv.org/abs/2301.00447v1
|
http://arxiv.org/pdf/2301.00447v1
| null | 2023-01-01T00:00:00
|
2023-01-01
|
Image To Tree with Recursive Prompting
|
James Batten; Matthew Sinclair; Ben Glocker; Michiel Schaap
| null | "Extracting complex structures from grid-based data is a common key step in automated medical image (...TRUNCATED)
|
cs.CV; cs.LG
|
cs.CV
|
12 pages, 5 figures
| null | null | 0
|
ArXiv
| [-0.32223746180534363,0.7417282462120056,-0.525932252407074,-0.11396943032741547,-1.3292663097381592(...TRUNCATED)
| [0.5826007127761841,0.7668974995613098,-0.17559315264225006,-0.2096143662929535,-0.6203295588493347,(...TRUNCATED)
|
[] | null | null |
2301.00411v2
|
http://arxiv.org/abs/2301.00411v2
|
http://arxiv.org/pdf/2301.00411v2
| null | 2023-01-01T00:00:00
|
2023-01-11
| "Detachable Novel Views Synthesis of Dynamic Scenes Using Distribution-Driven Neural Radiance Fiel(...TRUNCATED)
|
Boyu Zhang; Wenbo Xu; Zheng Zhu; Guan Huang
| null | "Representing and synthesizing novel views in real-world dynamic scenes from casual monocular videos(...TRUNCATED)
|
cs.CV
|
cs.CV
| null | null | null | 0
|
ArXiv
| [-0.6718947887420654,-1.1038111448287964,-0.4683847725391388,-0.6771073937416077,0.1377648562192917,(...TRUNCATED)
| [0.36005163192749023,-0.16463734209537506,-0.1901589035987854,-0.14248360693454742,0.297913759946823(...TRUNCATED)
|
[] | null | null |
2301.00364v1
|
http://arxiv.org/abs/2301.00364v1
|
http://arxiv.org/pdf/2301.00364v1
| null | 2023-01-01T00:00:00
|
2023-01-01
|
Generalizable Black-Box Adversarial Attack with Meta Learning
|
Fei Yin; Yong Zhang; Baoyuan Wu; Yan Feng; Jingyi Zhang; Yanbo Fan; Yujiu Yang
| null | "In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the(...TRUNCATED)
|
cs.LG; cs.CR; cs.CV
|
cs.LG
|
T-PAMI 2022. Project Page is at https://github.com/SCLBD/MCG-Blackbox
| null | null | 0
|
ArXiv
| [-1.7685387134552002,-1.2444485425949097,-0.45394864678382874,-0.17141945660114288,-0.87849491834640(...TRUNCATED)
| [-0.2950334846973419,0.14111705124378204,-0.4192160367965698,0.4233262836933136,-1.159401535987854,-(...TRUNCATED)
|
[] | null | null |
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