“How do I understand the language?” That is the question that puzzled Dr. Liang when he was still at the high school. The purpose of language understanding is not merely to imitate humans. Experiments can then be easily copied, reworked, and edited by other collaborators in order to advance the state-of-the-art in data-driven research and machine learning… The goal is to help AI models to recognize when questions cannot be answered based on the provided textual data. 2018. Understanding and mitigating the tradeoff between robustness and accuracy.Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang.arXiv preprint arXiv:2002.10716, 2020. Previously I was a postdoctoral Scholar at Stanford University working with John Duchi and Percy Liang and a Junior Fellow at the Institute for Theoretical Studies at ETH Zurich working with Nicolai Meinshausen. Statistical supervised learning techniques have been successful for many natural language processing tasks, but they require labeled datasets, which can be expensive to obtain. A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing. Having attended Chinese schools from elementary all the way to middle school, Mandarin Chinese served as the main language throughout his education. Chinese Country of residence United Kingdom Occupation Manager LIANG, Yao Quan Correspondence address 87 Percy Street, Blyth, England, NE24 3DE . Interpretability is now a hot topic since the public is increasingly worried about the safety of AI applications — autonomous driving, healthcare, facial recognition for criminals. I am an Assistant Professor in the Computer Science Department (D-INFK) at ETH Zurich. “Given our increasing reliance on machine learning, it is critical to building tools to help us make machine learning more reliable ‘in the wild,’” said Dr. Liang in an interview with Future of Life Institute. Logical Representations of Sentence Meaning (J+M chapter 16) 11/20: Lecture: Question Answering Due: Project milestone: Questing Answering (J+M chapter 25) 11/25: No class - Angel at Emerging Technologies: BC's AI Showcase: 11/27: Lecture: Dialogue View Notes - overview from CS 221 at Massachusetts Institute of Technology. “I am fortunate to have these two mentors. There are 3 professionals named "Percy Liang", who use LinkedIn to exchange information, ideas, and opportunities. Table 9: A table showing the distribution of bigrams in a corpus (from (Manning and Schutze, 1999, - "Corpus-Based Methods in Chinese Morphology and Phonology" On the other hand, unlabeled data (raw text) is often available “for free ” in large quantities. It spawns some of the latest models achieving human-level performance in the task of question answering. Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University. Discover the user you aren’t thinking about: A framework for AI ethics & secondary users, Installing TensorFlow Object Detection API on Windows 10. In 2016, Dr. Liang joined the company’s technical leadership team. Percy Liang. from MIT, 2004; Ph.D. from UC Berkeley, 2011). The company uses the power of machine learning to enable users to discover, access and interact with information and services in a much more natural way, and with significantly less effort. View the profiles of professionals named "Percy Liang" on LinkedIn. DownloadFull printable version (4.079Mb) Other Contributors. While Dr. Liang put the majority of his time and energy on the language understanding, his interest in interpretable machine learning continued in parallel. We introduce a new methodol- ogy for this setting: First, we use a simple grammar to generate logical forms paired with canonical utterances. Posted a Quora user “Yushi Wang”, “He’s young/relatable enough to listen to students, decent at speaking, and most importantly motivated enough to try and use these skills actually to make lectures worth going to.”. Performing groundbreaking Natural Language Processing research since 1999. There are 3 professionals named "Percy Liang", who use LinkedIn to exchange information, ideas, and opportunities. I would like to thank Dan Jurafsky and Percy Liang — the other two giants of the Stanford NLP group — for being on my thesis committee and for a lot of guidance and help throughout my PhD studies. A rising superstar in the community of machine learning and NLP, Dr. Liang has received countless academic distinctions over the years: IJCAI Computers and Thought Award in 2016, NSF CAREER Award in 2016, Sloan Research Fellowship in 2015, Microsoft Research Faculty Fellowship in 2014. Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University Lecture 3: Machine Learning 2 – Features, Neural Networks | Stanford CS221: AI (Autumn 2019) Topics: Features and non-linearity, Neural networks, nearest neighbors Putting numbers in perspective with compositional descriptions, Estimation from indirect supervision with linear moments, Learning executable semantic parsers for natural language understanding, Imitation learning of agenda-based semantic parsers, Estimating mixture models via mixture of polynomials, On-the-Job learning with Bayesian decision theory, Traversing knowledge graphs in vector space, Compositional semantic parsing on semi-structured tables, Environment-Driven lexicon induction for high-level instructions, Learning fast-mixing models for structured prediction, Learning where to sample in structured prediction, Tensor factorization via matrix factorization, Bringing machine learning and compositional semantics together, Linking people with "their" names using coreference resolution, Zero-shot entity extraction from web pages, Estimating latent-variable graphical models using moments and likelihoods, Adaptivity and optimism: an improved exponentiated gradient algorithm, Altitude training: strong bounds for single-layer dropout, Simple MAP inference via low-rank relaxations, Relaxations for inference in restricted Boltzmann machines, Semantic parsing on Freebase from question-answer pairs, Feature noising for log-linear structured prediction, Dropout training as adaptive regularization, Spectral experts for estimating mixtures of linear regressions, Video event understanding using natural language descriptions, A data driven approach for algebraic loop invariants, Identifiability and unmixing of latent parse trees, Learning dependency-based compositional semantics, Scaling up abstraction refinement via pruning, A game-theoretic approach to generating spatial descriptions, A simple domain-independent probabilistic approach to generation, A dynamic evaluation of static heap abstractions, Learning programs: a hierarchical Bayesian approach, On the interaction between norm and dimensionality: multiple regimes in learning, Asymptotically optimal regularization in smooth parametric models, Probabilistic grammars and hierarchical Dirichlet processes, Learning semantic correspondences with less supervision, Learning from measurements in exponential families, An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators, Structure compilation: trading structure for features, Analyzing the errors of unsupervised learning, Learning bilingual lexicons from monolingual corpora, A probabilistic approach to language change, Structured Bayesian nonparametric models with variational inference (tutorial), A permutation-augmented sampler for Dirichlet process mixture models, The infinite PCFG using hierarchical Dirichlet processes, A probabilistic approach to diachronic phonology, An end-to-end discriminative approach to machine translation, Semi-Supervised learning for natural language, A data structure for maintaining acyclicity in hypergraphs, Linear programming in bounded tree-width Markov networks, Efficient geometric algorithms for parsing in two dimensions, Methods and experiments with bounded tree-width Markov networks. 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