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2020 – today
- 2024
[c24]Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, Sanjiv Kumar:
Rethinking FID: Towards a Better Evaluation Metric for Image Generation. CVPR 2024: 9307-9315- [c23]Sadeep Jayasumana, Daniel Glasner, Srikumar Ramalingam, Andreas Veit, Ayan Chakrabarti, Sanjiv Kumar:
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation. CVPR 2024: 9316-9325 - [i28]Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, Sanjiv Kumar:
Rethinking FID: Towards a Better Evaluation Metric for Image Generation. CoRR abs/2401.09603 (2024) - [i27]Ziwei Ji, Himanshu Jain, Andreas Veit, Sashank J. Reddi, Sadeep Jayasumana, Ankit Singh Rawat, Aditya Krishna Menon, Felix Yu, Sanjiv Kumar:
Efficient Document Ranking with Learnable Late Interactions. CoRR abs/2406.17968 (2024) - 2023
- [c22]Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix X. Yu, Sanjiv Kumar:
Large Language Models with Controllable Working Memory. ACL (Findings) 2023: 1774-1793 - [c21]Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar:
Teacher Guided Training: An Efficient Framework for Knowledge Transfer. ICLR 2023 - [i26]Sadeep Jayasumana, Daniel Glasner, Srikumar Ramalingam, Andreas Veit, Ayan Chakrabarti, Sanjiv Kumar:
SPEGTI: Structured Prediction for Efficient Generative Text-to-Image Models. CoRR abs/2308.10997 (2023) - 2022
- [i25]Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar:
Teacher Guided Training: An Efficient Framework for Knowledge Transfer. CoRR abs/2208.06825 (2022) - [i24]Arslan Chaudhry, Aditya Krishna Menon, Andreas Veit, Sadeep Jayasumana, Srikumar Ramalingam, Sanjiv Kumar:
When does mixup promote local linearity in learned representations? CoRR abs/2210.16413 (2022) - [i23]Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix X. Yu, Sanjiv Kumar:
Large Language Models with Controllable Working Memory. CoRR abs/2211.05110 (2022) - 2021
- [c20]Sashank J. Reddi, Rama Kumar Pasumarthi, Aditya Krishna Menon, Ankit Singh Rawat, Felix X. Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar:
RankDistil: Knowledge Distillation for Ranking. AISTATS 2021: 2368-2376 - [c19]Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner
, Andreas Veit:
Understanding Robustness of Transformers for Image Classification. ICCV 2021: 10211-10221 - [c18]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar:
Long-tail learning via logit adjustment. ICLR 2021 - [c17]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. ICLR 2021 - [i22]Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit, Ankit Singh Rawat, Seungyeon Kim, Aditya Krishna Menon, Sanjiv Kumar:
On the Reproducibility of Neural Network Predictions. CoRR abs/2102.03349 (2021) - [i21]Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, Andreas Veit:
Understanding Robustness of Transformers for Image Classification. CoRR abs/2103.14586 (2021) - [i20]Srinadh Bhojanapalli, Ayan Chakrabarti, Himanshu Jain, Sanjiv Kumar, Michal Lukasik, Andreas Veit:
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation. CoRR abs/2106.08823 (2021) - [i19]Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit, Michal Lukasik, Himanshu Jain, Frederick Liu, Yin-Wen Chang, Sanjiv Kumar:
Leveraging redundancy in attention with Reuse Transformers. CoRR abs/2110.06821 (2021) - 2020
- [j2]Andreas Veit, Serge J. Belongie:
Convolutional Networks with Adaptive Inference Graphs. Int. J. Comput. Vis. 128(3): 730-741 (2020) - [c16]Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov:
How To Backdoor Federated Learning. AISTATS 2020: 2938-2948 - [c15]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why are Adaptive Methods Good for Attention Models? NeurIPS 2020 - [i18]Ankit Singh Rawat, Aditya Krishna Menon, Andreas Veit, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Doubly-stochastic mining for heterogeneous retrieval. CoRR abs/2004.10915 (2020) - [i17]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar:
Long-tail learning via logit adjustment. CoRR abs/2007.07314 (2020) - [i16]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. CoRR abs/2010.12230 (2020) - [i15]Andreas Veit, Kimberly Wilber:
Improving Calibration in Deep Metric Learning With Cross-Example Softmax. CoRR abs/2011.08824 (2020)
2010 – 2019
- 2019
- [i14]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why ADAM Beats SGD for Attention Models. CoRR abs/1912.03194 (2019) - 2018
- [c14]Yin Cui, Guandao Yang, Andreas Veit, Xun Huang, Serge J. Belongie:
Learning to Evaluate Image Captioning. CVPR 2018: 5804-5812 - [c13]Andreas Veit, Maximilian Nickel, Serge J. Belongie, Laurens van der Maaten:
Separating Self-Expression and Visual Content in Hashtag Supervision. CVPR 2018: 5919-5927 - [c12]Andreas Veit, Serge J. Belongie:
Convolutional Networks with Adaptive Inference Graphs. ECCV (1) 2018: 3-18 - [i13]Yin Cui, Guandao Yang, Andreas Veit, Xun Huang, Serge J. Belongie:
Learning to Evaluate Image Captioning. CoRR abs/1806.06422 (2018) - [i12]Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov:
How To Backdoor Federated Learning. CoRR abs/1807.00459 (2018) - [i11]Isay Katsman, Rohun Tripathi, Andreas Veit, Serge J. Belongie:
Semantic Segmentation with Scarce Data. CoRR abs/1807.00911 (2018) - 2017
- [c11]Andreas Veit, Serge J. Belongie, Theofanis Karaletsos:
Conditional Similarity Networks. CVPR 2017: 1781-1789 - [c10]Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, Serge J. Belongie:
Learning from Noisy Large-Scale Datasets with Minimal Supervision. CVPR 2017: 6575-6583 - [c9]Raul Gomez, Baoguang Shi, Lluis Gomez-Bigorda, Lukás Neumann, Andreas Veit, Jiri Matas, Serge J. Belongie, Dimosthenis Karatzas:
ICDAR2017 Robust Reading Challenge on COCO-Text. ICDAR 2017: 1435-1443 - [c8]Rajan Vaish, Snehalkumar (Neil) S. Gaikwad, Geza Kovacs, Andreas Veit, Ranjay Krishna, Imanol Arrieta Ibarra, Camelia Simoiu, Michael J. Wilber, Serge J. Belongie, Sharad Goel, James Davis, Michael S. Bernstein:
Crowd Research: Open and Scalable University Laboratories. UIST 2017: 829-843 - [i10]Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, Serge J. Belongie:
Learning From Noisy Large-Scale Datasets With Minimal Supervision. CoRR abs/1701.01619 (2017) - [i9]David Rolnick, Andreas Veit, Serge J. Belongie, Nir Shavit:
Deep Learning is Robust to Massive Label Noise. CoRR abs/1705.10694 (2017) - [i8]Andreas Veit, Maximilian Nickel, Serge J. Belongie, Laurens van der Maaten:
Separating Self-Expression and Visual Content in Hashtag Supervision. CoRR abs/1711.09825 (2017) - [i7]Andreas Veit, Serge J. Belongie:
Convolutional Networks with Adaptive Computation Graphs. CoRR abs/1711.11503 (2017) - 2016
- [c7]Hani Altwaijry, Andreas Veit, Serge J. Belongie:
Learning to Detect and Match Keypoints with Deep Architectures. BMVC 2016 - [c6]Andreas Veit, Michael J. Wilber, Serge J. Belongie:
Residual Networks Behave Like Ensembles of Relatively Shallow Networks. NIPS 2016: 550-558 - [i6]Andreas Veit, Tomas Matera, Lukás Neumann, Jiri Matas, Serge J. Belongie:
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images. CoRR abs/1601.07140 (2016) - [i5]Andreas Veit, Serge J. Belongie, Theofanis Karaletsos:
Disentangling Nonlinear Perceptual Embeddings With Multi-Query Triplet Networks. CoRR abs/1603.07810 (2016) - [i4]Andreas Veit, Michael J. Wilber, Serge J. Belongie:
Residual Networks are Exponential Ensembles of Relatively Shallow Networks. CoRR abs/1605.06431 (2016) - 2015
- [c5]Andreas Veit, Balazs Kovacs, Sean Bell, Julian J. McAuley, Kavita Bala, Serge J. Belongie:
Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences. ICCV 2015: 4642-4650 - [c4]Andreas Veit:
MDSM: Generalized multiagent coordination for demand side management. SmartGridComm 2015: 73-78 - [c3]Andreas Veit, Hans-Arno Jacobsen:
Multi-agent device-level modeling framework for demand scheduling. SmartGridComm 2015: 169-174 - [i3]Andreas Veit, Balazs Kovacs, Sean Bell, Julian J. McAuley, Kavita Bala, Serge J. Belongie:
Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences. CoRR abs/1509.07473 (2015) - [i2]Andreas Veit, Michael J. Wilber, Rajan Vaish, Serge J. Belongie, James Davis, Vishal Anand, Anshu Aviral, Prithvijit Chakrabarty, Yash Chandak, Sidharth Chaturvedi, Chinmaya Devaraj, Ankit Dhall, Utkarsh Dwivedi, Sanket Gupte, Sharath N. Sridhar, Karthik Paga, Anuj Pahuja, Aditya Raisinghani, Ayush Sharma, Shweta Sharma, Darpana Sinha, Nisarg Thakkar, K. Bala Vignesh, Utkarsh Verma, Kanniganti Abhishek, Amod Agrawal, Arya Aishwarya, Aurgho Bhattacharjee, Sarveshwaran Dhanasekar, Venkata Karthik Gullapalli, Shuchita Gupta, Chandana G, Kinjal Jain, Simran Kapur, Meghana Kasula, Shashi Kumar, Parth Kundaliya, Utkarsh Mathur, Alankrit Mishra, Aayush Mudgal, Aditya Nadimpalli, Munakala Sree Nihit, Akanksha Periwal, Ayush Sagar, Ayush Shah, Vikas Sharma, Yashovardhan Sharma, Faizal Siddiqui, Virender Singh, Abhinav S., Pradyumna Tambwekar, Rashida Taskin, Ankit Tripathi, Anurag D. Yadav:
On Optimizing Human-Machine Task Assignments. CoRR abs/1509.07543 (2015) - 2014
- [j1]Andreas Veit, Ying Xu, Ronghuo Zheng, Nilanjan Chakraborty, Katia P. Sycara:
Demand Side Energy Management via Multiagent Coordination in Consumer Cooperatives. J. Artif. Intell. Res. 50: 885-922 (2014) - [c2]Andreas Veit, Christoph Goebel, Rohit Tidke, Christoph Doblander, Hans-Arno Jacobsen:
Household electricity demand forecasting: benchmarking state-of-the-art methods. e-Energy 2014: 233-234 - [i1]Andreas Veit, Christoph Goebel, Rohit Tidke, Christoph Doblander, Hans-Arno Jacobsen:
Household Electricity Demand Forecasting - Benchmarking State-of-the-Art Methods. CoRR abs/1404.0200 (2014) - 2013
- [c1]Andreas Veit, Ying Xu, Ronghuo Zheng, Nilanjan Chakraborty, Katia P. Sycara:
Multiagent Coordination for Energy Consumption Scheduling in Consumer Cooperatives. AAAI 2013: 1362-1368
Coauthor Index
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last updated on 2024-10-04 20:02 CEST by the dblp team
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