Documents dont l'auteur est "Anbil Parthipan, Sarath Chandar"

Sarath Chandar Anbil ParthipanDépartement de génie informatique et génie logicielVoir sa fiche dans le Répertoire des Expertises de Polytechnique Montréal

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Abdelsalam, M., Faramarzi, M., Sodhani, S., & Anbil Parthipan, S. C. (juin 2021). IIRC: Incremental Implicitly-Refined Classification [Communication écrite]. Conference on Computer Vision and Pattern Recognition (CVPR) (31 pages). Lien externe

Anbil Parthipan, S. C. (2019). On challenges in training recurrent neural networks [Thèse de doctorat, Université de Montréal]. Lien externe

Anbil Parthipan, S. C., Sankar, C., Vorontsov, E., Kahou, S. E., & Bengio, Y. (2019). Towards Non-Saturating Recurrent Units for Modelling Long-Term Dependencies. AAAI Conference on Artificial Intelligence, 33(1), 3280-3287. Lien externe

Anbil Parthipan, S. C., Khapra, M. M., Larochelle, H., & Ravindran, B. (2016). Correlational Neural Networks. Neural Computation, 28(2), 257-285. Lien externe

Anbil Parthipan, S. C. (2015). Correlational Neural Networks for Common Representation Learning [Mémoire de maîtrise, Indian Institute of Technology Madras]. Non disponible

Anbil Parthipan, S. C., Lauly, S., Larochelle, H., Khapra, M. M., Ravindran, B., Raykar, V., & Saha, A. (décembre 2014). An autoencoder approach to learning bilingual word representations [Communication écrite]. 27th International Conference on Neural Information Processing Systems, Montréal, Qc, Canada. Lien externe

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Bard, N., Foerster, J. N., Anbil Parthipan, S. C., Burch, N., Lanctot, M., Song, H. F., Parisotto, E., Dumoulin, V., Moitra, S., Hughes, E., Dunning, I., Mourad, S., Larochelle, H., Bellemare, M. G., & Bowling, M. (2020). The Hanabi challenge: A new frontier for AI research. Artificial Intelligence, 280, 19 pages. Lien externe

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Clouatre, L., Parthasarathi, P., Zouaq, A., & Anbil Parthipan, S. C. (mai 2022). Local Structure Matters Most: Perturbation Study in NLU [Communication écrite]. 60th Annual Meeting of the Association-for-Computational-Linguistics (ACL 2022), Dublin, IRELAND. Lien externe

Clouatre, L., Trempe, P., Zouaq, A., & Anbil Parthipan, S. C. (août 2021). MLMLM: link prediction with mean likelihood masked language model [Communication écrite]. The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), Bangkok, Thailand. Disponible

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De Vries, H., Strub, F., Anbil Parthipan, S. C., Pietquin, O., Larochelle, H., & Courville, A. (juillet 2017). GuessWhat?! Visual Object Discovery through Multi-modal Dialogue [Communication écrite]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, USA. Lien externe

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Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., & Anbil Parthipan, S. C. (février 2022). PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks [Communication écrite]. 36th AAAI Conference on Artificial Intelligence (AAAI 2022). Lien externe

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Gottipati, S. K., Pathak, Y., Sattarov, B., Sahir, Nuttall, R., Amini, M., Taylor, M. E., & Anbil Parthipan, S. C. (février 2021). Towered Actor Critic For Handling Multiple Action Types In Reinforcement Learning For Drug Discovery [Communication écrite]. 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence. Lien externe

Gottipati, S. K., Sattarov, B., Niu, S., Pathak, Y., Wei, H., Liu, S., Thomas, K. M. J., Blackburn, S., Coley, C. W., Tang, J., Anbil Parthipan, S. C., & Bengio, Y. (juillet 2020). Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. [Communication écrite]. 37th International Conference on Machine Learning (ICML 2020), Vienna, Austria. Lien externe

Gottipati, S. K., Pathak, Y., Nuttall, R., Sahir, Chunduru, R., Touati, A., Subramanian, S. G., Taylor, M. E., & Anbil Parthipan, S. C. (décembre 2020). Maximum reward formulation in reinforcement learning [Communication écrite]. 2020 NeurIPS Deep RL Workshop (15 pages). Lien externe

Gulcehre, C., Anbil Parthipan, S. C., Cho, K., & Bengio, Y. (2018). Dynamic neural turing machine with continuous and discrete addressing schemes. Neural Computation, 30(4), 857-884. Lien externe

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Lafleur, D., Anbil Parthipan, S. C., & Pesant, G. (juillet 2022). Combining reinforcement learning and constraint programming for sequence-generation tasks with hard constraints [Communication écrite]. 28th International Conference on Principles and Practice of Constraint Programming (CP 2022), Haifa, Israel. Lien externe

Laleh, T., Faramarzi, M., Rish, I., & Anbil Parthipan, S. C. (juillet 2020). Chaotic continual learning [Communication écrite]. 37th International Conference on Machine Learning (PMLR 2020), Vienna, Austria (6 pages). Lien externe

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Madsen, A., Anbil Parthipan, S. C., & Reddy, S. (août 2024). Are self-explanations from Large Language Models faithful? [Communication écrite]. 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), Hybrid, Bangkok, Thailand. Lien externe

Madsen, A., Reddy, S., & Anbil Parthipan, S. C. (juillet 2024). Faithfulness Measurable Masked Language Models [Communication écrite]. 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria. Lien externe

Mordido, G., Malviya, P., Baratin, A., & Anbil Parthipan, S. C. (juillet 2024). Lookbehind-SAM: k Steps Back, 1 Step Forward [Communication écrite]. 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria. Lien externe

Madsen, A., Reddy, S., & Anbil Parthipan, S. C. (2023). Post-hoc Interpretability for Neural NLP: A Survey. ACM Computing Surveys, 55(8), 1-42. Lien externe

McRae, P.-A., Parthasarathi, P., Assran, M., & Anbil Parthipan, S. C. (avril 2022). Memory augmented optimizers for deep learning [Affiche]. 10th International Conference on Learning Representations (ICLR 2022). Lien externe

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Nekoei, H., Badrinaaraayanan, A., Sinha, A., Amini, M., Rajendran, J., Mahajan, A., & Anbil Parthipan, S. C. (août 2023). Dealing with non-stationarity in decentralized cooperative multi-agent deep reinforcement learning via multi-timescale learning [Communication écrite]. 2nd Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Qc. Canada. Non disponible

Nekoei, H., Zhao, X. T., Rajendran, J., Liu, M. A., & Anbil Parthipan, S. C. (août 2023). Towards few-shot coordination : revisiting ad-hoc teamplay challenge in the game of Hanabi [Communication écrite]. 2nd Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Qc, Canada. Non disponible

Nekoei, H., Badrinaaraayanan, A., Courville, A., & Anbil Parthipan, S. C. (juillet 2021). Continuous Coordination As a Realistic Scenario for Lifelong Learning [Communication écrite]. International Conference on Machine Learning (ICML 2021). Lien externe

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Olewicki, D., Habchi, S., Nayrolles, M., Faramarzi, M., Anbil Parthipan, S. C., & Adams, B. (avril 2024). On the Costs and Benefits of Adopting Lifelong Learning for Software Analytics - Empirical Study on Brown Build and Risk Prediction [Communication écrite]. 46th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP 2024), Lisbon, Portugal. Lien externe

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Parthasarathi, P., Abdelsalam, M., Anbil Parthipan, S. C., & Pineau, J. (juillet 2021). A brief study on the effects of training generative dialogue models with a semantic loss [Communication écrite]. 22nd Annual Meeting of the Special-Interest-Group-on-Discourse-and-Dialogue (SIGDIAL 2021), Singapore, Singapore. Lien externe

Prato, G., Duchesneau, M., Anbil Parthipan, S. C., & Tapp, A. (juillet 2019). Towards Lossless Encoding of Sentences [Communication écrite]. 57th annual meeting of the Association for Computational Linguistics (ACL), Florence, Italy. Lien externe

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Rahimi-Kalahroudi, A., Rajendran, J., Momennejad, I., van Seijen, H., & Anbil Parthipan, S. C. (août 2023). Replay buffer with local forgetting for adapting to local environment changes in deep model-based reinforcement learning [Communication écrite]. 2nd Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Qc, Canada. Lien externe

Reddy, R., Anbil Parthipan, S. C., & Ravindran, B. (mai 2019). Edge Replacement Grammars : A Formal Language Approach for Generating Graphs [Communication écrite]. SIAM International Conference on Data Mining (SDM 2019), Calgary, Alberta, Canada. Lien externe

Rajendran, J., Khapra, M. M., Anbil Parthipan, S. C., & Ravindran, B. (juin 2016). Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning [Communication écrite]. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, California. Lien externe

Rongali, S., Anbil Parthipan, S. C., & Ravindran, B. (mars 2015). From multiple views to single view: a neural network approach [Communication écrite]. 2nd ACM IKDD Conference on Data Sciences, Bangalore, India. Lien externe

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Saha, A., Pahuja, V., Khapra, M. M., Sankaranarayanan, K., & Anbil Parthipan, S. C. (février 2018). Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph [Communication écrite]. 32nd AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, Louisiana. Lien externe

Saha, A., Khapra, M. M., Anbil Parthipan, S. C., Rajendran, J., & Cho, K. (décembre 2016). A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation [Communication écrite]. 26th International Conference on Computational Linguistics (COLING 2016), Osaka, Japan. Lien externe

Serban, I. V., García-Durán, A., Gulcehre, C., Ahn, S., Anbil Parthipan, S. C., Courville, A., & Bengio, Y. (août 2016). Generating Factoid Questions with Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus [Communication écrite]. 54th annual meeting of the Association for Computational Linguistics, Berlin, Germany. Lien externe

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Thakkar, M., Bolukbasi, T., Ganapathy, S., Vashishth, S., Anbil Parthipan, S. C., & Talukdar, P. (décembre 2023). Self-Influence Guided Data Reweighting for Language Model Pre-training [Communication écrite]. Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), Singapore. Lien externe

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Vaibhav Mehta, S., Patil, D., Anbil Parthipan, S. C., & Strubell, E. (2023). An Empirical Investigation of the Role of Pre-training in Lifelong Learning. Journal of Machine Learning Research, 24, 50-50. Lien externe

Van Seijen, H., Nekoei, H., Racah, E., & Anbil Parthipan, S. C. (décembre 2020). The LoCA regret: A consistent metric to evaluate model-based behavior in reinforcement learning [Communication écrite]. 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Non disponible

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Wan, Y., Rahimi-Kalahroudi, A., Rajendran, J., Momennejad, I., Anbil Parthipan, S. C., & van Seijen, H. (juillet 2022). Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods [Communication écrite]. 39th International Conference on Machine Learning (ICML 2022), Baltimore, MD. Lien externe

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Zayed, A., Mordido, G., Baldini, I., & Anbil Parthipan, S. C. (août 2024). Why Don't Prompt-Based Fairness Metrics Correlate? [Communication écrite]. 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), Bangkok, Thailand. Lien externe

Zhao, X., Pan, Y., Xiao, C., Anbil Parthipan, S. C., & Rajendran, J. (juillet 2023). Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning [Communication écrite]. 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023), Pittsburgh, PA, USA. Lien externe

Zayed, A., Parthasarathi, P., Mordido, G., Palangi, H., Shabanian, S., & Anbil Parthipan, S. C. (février 2023). Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness [Communication écrite]. 37th AAAI Conference on Artificial Intelligence (AAAI 2023) and 35th Conference on Innovative Applications of Artificial Intelligence (IAAI 2023) and 13th Symposium on Educational Advances in Artificial Intelligence (EAAI 2023), Washington, DC, USA. Lien externe