Andrew M. Dai
Andrew is a software engineer at Google. Prior to that he completed a PhD in machine learning at the University of Edinburgh and a MA in computer science at the University of Cambridge.
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MaMMUT: A Simple Vision-Encoder Text-Decoder Architecture for MultiModal Tasks
Wei Li
Abhijit Ogale
Luowei Zhou
Transactions on Machine Learning Research (2023)
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The development of language models have moved from encoder-decoder to decoder-only designs. In addition, the common knowledge has it that the two most popular multimodal tasks, the generative and contrastive tasks, tend to conflict with one another, are hard to accommodate in one architecture, and further need complex adaptations for downstream tasks. We propose a novel paradigm of training with a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks. This is done with a simple model, called MaMMUT. It consists of a single vision encoder and a text decoder, and is able to accommodate contrastive and generative learning by a novel two-pass approach on the text decoder. We demonstrate that joint learning of these diverse objectives is simple, effective, and maximizes the weight-sharing of the model across these tasks. Furthermore, the same architecture enables straightforward extensions to open-vocabulary object detection and video-language tasks. The model tackles a diverse range of tasks, while being modest in capacity. Our model achieves the state of the art on image-text and text-image retrieval, video question answering and open-vocabulary detection tasks, outperforming much larger and more extensively trained foundational models. It shows very competitive results on VQA and Video Captioning, especially considering its capacity. Ablations confirm the flexibility and advantages of our approach.
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Sparsely Activated Language Models are Efficient In-Context Learners
Barret Richard Zoph
Dmitry (Dima) Lepikhin
Emma Wang
Kathy Meier-Hellstern
Kun Zhang
Liam B. Fedus
Maarten Paul Bosma
Marie Pellat
Maxim Krikun
Nan Du
Simon Tong
Tao Wang
Toju Duke
Yuanzhong Xu
Zongwei Zhou
(2022)
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Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong performance on few-shot learning. However, training these large dense models require significant amounts of computing resources. In this paper, we develop a family of sparsely activated mixture-of-expert language models named \glam (\textbf{G}eneralist \textbf{La}nguage \textbf{M}odel), which can have many more parameters but require significant less training cost than dense models. The largest \glam has 1.2 trillion parameters, which is approximately 7x larger than GPT-3 but can be trained more efficiently. With only 1/3 of energy consumption to train GPT-3, \glam achieves better overall performance on 29 zero-shot and one-shot NLP tasks. For example, \glam gets 75.0\% one-shot exact match accuracy on the TriviaQA test server, a significant improvement over 68.0\% obtained by GPT-3.
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Finetuned Language Models are Zero-Shot Learners
Jason Wei
Maarten Paul Bosma
Vincent Zhao
Nan Du
International Conference on Learning Representations (2022)
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This paper explores a simple method for improving the zero-shot learning abilities of language models.
We show that instruction tuning---finetuning language models on a collection of tasks described via instructions---substantially boosts zero-shot performance on unseen tasks.
We take a 137B parameter pretrained language model and instruction-tune it on over 60 NLP tasks verbalized via natural language instruction templates. We evaluate this instruction-tuned model, which we call FLAN, on unseen task types. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 tasks that we evaluate. FLAN even outperforms few-shot GPT-3 by a large margin on ANLI, RTE, BoolQ, AI2-ARC, OpenbookQA, and StoryCloze. Ablation studies reveal that number of tasks and model scale are key components to the success of instruction tuning.
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PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery
Sharan Narang
Jacob Devlin
Maarten Bosma
Hyung Won Chung
Sebastian Gehrmann
Parker Schuh
Sasha Tsvyashchenko
Abhishek Rao
Yi Tay
Noam Shazeer
Nan Du
Reiner Pope
James Bradbury
Guy Gur-Ari
Toju Duke
Henryk Michalewski
Xavier Garcia
Liam Fedus
David Luan
Barret Zoph
Ryan Sepassi
David Dohan
Shivani Agrawal
Mark Omernick
Marie Pellat
Aitor Lewkowycz
Erica Moreira
Rewon Child
Oleksandr Polozov
Zongwei Zhou
Brennan Saeta
Michele Catasta
Jason Wei
Kathy Meier-Hellstern
arxiv:2204.02311 (2022)
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Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.
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Mind's Eye: Grounded Language Model Reasoning through Simulation
Jason Wei
Shixiang Shane Gu
Soroush Vosoughi
ICLR 2023 (2022)
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Successful and effective communication between humans and AI relies on a shared experience of the world. By training solely on written text, current language models (LMs) miss the grounded experience of humans in the real-world—their failure to relate language to the physical world causes knowledge to be misrepresented and obvious mistakes in their reasoning. We present Mind's Eye, a paradigm to ground language model reasoning in the physical world. Given a physical reasoning question, we use a computational physics engine (DeepMind’s MuJoCo) to simulate the possible outcomes, and then use the simulation results as part of the input, which enables language models to perform reasoning. Experiments on 39 tasks in a physics alignment benchmark demonstrate that Mind's Eye can improve reasoning ability by a large margin (27.9% zero-shot, and 46.0% few-shot absolute accuracy improvement on average). Smaller language models armed with Mind's Eye can obtain similar performance to models that are 100× larger. Finally, we confirm the robustness of Mind's Eye through ablation studies.
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Impacts of social distancing policies on mobility and COVID-19 case growth in the US
Gregory Alexander Wellenius
Swapnil Suresh Vispute
Valeria Espinosa
Thomas Tsai
Jonathan Hennessy
Krishna Kumar Gadepalli
Adam Boulanger
Adam Pearce
Chaitanya Kamath
Arran Schlosberg
Catherine Bendebury
Chinmoy Mandayam
Charlotte Stanton
Shailesh Bavadekar
Christopher David Pluntke
Damien Desfontaines
Benjamin H. Jacobson
Zan Armstrong
Katherine Chou
Andrew Nathaniel Oplinger
Ashish K. Jha
Evgeniy Gabrilovich
Nature Communications (2021)
Training independent subnetworks for robust prediction
Marton Havasi
Rodolphe Jenatton
Stanislav Fort
International Conference on Learning Representations (2021)
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Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network. However, these methods still require multiple forward passes for prediction, leading to a significant runtime cost. In this work, we show a surprising result: the benefits of using multiple predictions can be achieved 'for free' under a single model's forward pass. In particular, we show that, using a multi-input multi-output (MIMO) configuration, one can utilize a single model's capacity to train multiple subnetworks that independently learn the task at hand. By ensembling the predictions made by the subnetworks, we improve model robustness without increasing compute. We observe a significant improvement in negative log-likelihood, accuracy, and calibration error on CIFAR10, CIFAR100, ImageNet, and their out-of-distribution variants compared to previous methods.
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Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning. In this work, we propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events (e.g., kidney failure, mortality) by explicitly modeling the temporal dynamics of patients' latent states. Based on these learned patient states, we further develop a new general discrete-time formulation of the hazard rate function to estimate the survival distribution of patients with significantly improved accuracy. Extensive evaluations over real EMR data show that our proposed model compares favorably to various state-of-the-art baselines. Furthermore, our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
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Learnability and Complexity of Quantum Samples
Li Li
Augustus Odena
Zhengli Zhao
Vadim Smelyanskiy
arxiv (2020)
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Given a quantum circuit, a quantum computer can sample the output distribution exponentially
faster in the number of bits than classical computers. A similar exponential separation has yet
to be established in generative models through quantum sample learning: given samples from
an n-qubit computation, can we learn the underlying quantum distribution using models with
training parameters that scale polynomial in n under a fixed training time? We study four kinds of
generative models: Deep Boltzmann machine (DBM), Generative Adversarial Networks (GANs),
Long Short-Term Memory (LSTM) and Autoregressive GAN, on learning quantum data set generated
by deep random circuits. We demonstrate the leading performance of LSTM in learning quantum
samples, and thus the autoregressive structure present in the underlying quantum distribution from
random quantum circuits. Both numerical experiments and a theoretical proof in the case of the
DBM show exponentially growing complexity of learning-agent parameters required for achieving
a fixed accuracy as n increases. Finally, we establish a connection between learnability and the
complexity of generative models by benchmarking learnability against different sets of samples drawn
from probability distributions of variable degrees of complexities in their quantum and classical
representations.
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Predicting inpatient medication orders from electronic health record data
Kathryn Rough
Kun Zhang
Atul J. Butte
Alvin Rajkomar
Clinical Pharmacology and Therapeutics (2020)
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In a general inpatient population, we predicted patient‐specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine‐learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most frequently ordered medications based on a patient’s discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty‐five percent of medications ordered by physicians were ranked in the sequence model’s top‐10 predictions (logistic model: 49%) and 75% ranked in the top‐25 (logistic model: 69%). Ninety‐three percent of the sequence model’s top‐10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the EHR.
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