9/22/2023 0 Comments Jing ling zhang![]() Opinion Tree Parsing for Aspect-based Sentiment Analysis Namo Bang, Jeehyun Lee and Myoung-Wan Koo Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System Nishant Balepur, Shivam Agarwal, Karthik Venkat Ramanan, Susik Yoon, Diyi Yang and Jiawei Han Yujin Baek, Koanho Lee, Dayeon Ki, Cheonbok Park, Hyoung-Gyu Lee and Jaegul ChooĭynaMiTE: Discovering Explosive Topic Evolutions with User Guidance Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints Kartikeya Badola, Shachi Dave and Partha Talukdar Parameter-Efficient Finetuning for Robust Continual Multilingual Learning Naveen Badathala, Abisek Rajakumar Kalarani, Tejpalsingh Siledar and Pushpak Bhattacharyya Gaurav Arora, Srujana Merugu and Vivek SembiumĪkari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi and Wen-tau YihĪ Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection Vladimir Araujo, Alvaro Soto and Marie-Francine MoensĬoMix: Guide Transformers to Code-Mix using POS structure and Phonetics Rahul Aralikatte, Ziling Cheng, Sumanth Doddapaneni and Jackie Chi Kit CheungĪ Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information Varta: A Large-Scale Headline-Generation Dataset for Indic Languages Miriam Anschütz, Joshua Oehms, Thomas Wimmer, Bartłomiej Jezierski and Georg Grohĭistilling Efficient Language-Specific Models for Cross-Lingual TransferĪlan Ansell, Edoardo Maria Ponti, Anna Korhonen and Ivan Vulić Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training Impact of Adversarial Training on Robustness and Generalizability of Language ModelsĮnes Altinisik, Hassan Sajjad, Husrev Sencar, Safa Messaoud and Sanjay Chawla Improving Diachronic Word Sense Induction with a Nonparametric Bayesian method Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings Multilingual Pre-training with Self-supervision from Global Co-occurrence Information Shafiuddin Rehan Ahmed, Abhijnan Nath, James H. $2*n$ is better than $n^2$: Decomposing Event Coreference Resolution into Two Tractable Problems Roee Aharoni, Shashi Narayan, Joshua Maynez, Jonathan Herzig, Elizabeth Clark and Mirella Lapata ![]() Multilingual Summarization with Factual Consistency Evaluation Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer and Marjan Ghazvininejad In-context Examples Selection for Machine Translation Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed and Alcides Alcoba Inciarte Theory holds across different training settings.SERENGETI: Massively Multilingual Language Models for Africa Over-parameterized networks to the neural tangent kernel. Our theoretical analysis builds on a connection of Of GNNs in extrapolating algorithmic tasks to new data (e.g., larger graphs orĮdge weights) relies on encoding task-specific non-linearities in theĪrchitecture or features. Hypothesis for which we provide theoretical and empirical evidence: the success Second, in connection toĪnalyzing the successes and limitations of GNNs, these results suggest a Training distribution is sufficiently "diverse". But, they can provably learn a linear target function when the Origin, which implies that ReLU MLPs do not extrapolate most nonlinearįunctions. ReLU MLPs quickly converge to linear functions along any direction from the Working towards a theoretical explanation, we identify conditions under ![]() Structured networks with MLP modules - have shown some success in more complex multilayer perceptrons (MLPs), do notĮxtrapolate well in certain simple tasks, Graph Neural Networks (GNNs). While feedforward neural networks, a.k.a. Works report mixed empirical results when extrapolating with neural networks: What they learn outside the support of the training distribution. ![]() Download a PDF of the paper titled How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks, by Keyulu Xu and 5 other authors Download PDF Abstract: We study how neural networks trained by gradient descent extrapolate, i.e.,
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |