Search Results for author: Joseph E. Gonzalez

Found 104 papers, 53 papers with code

LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset

no code implementations21 Sep 2023 Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric. P Xing, Joseph E. Gonzalez, Ion Stoica, Hao Zhang

Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications.

Chatbot Instruction Following

Efficient Memory Management for Large Language Model Serving with PagedAttention

2 code implementations12 Sep 2023 Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, Ion Stoica

On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage.

Language Modelling Large Language Model +1

Judging LLM-as-a-judge with MT-Bench and Chatbot Arena

2 code implementations9 Jun 2023 Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, Ion Stoica

Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences.

Chatbot Language Modelling +1

Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation

1 code implementation25 May 2023 Lisa Dunlap, Alyssa Umino, Han Zhang, Jiezhi Yang, Joseph E. Gonzalez, Trevor Darrell

We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing.

Image Augmentation

Decomposing Complex Queries for Tip-of-the-tongue Retrieval

no code implementations24 May 2023 Kevin Lin, Kyle Lo, Joseph E. Gonzalez, Dan Klein

When re-finding items, users who forget or are uncertain about identifying details often rely on creative strategies for expressing their information needs -- complex queries that describe content elements (e. g., book characters or events), information beyond the document text (e. g., descriptions of book covers), or personal context (e. g., when they read a book).


Gorilla: Large Language Model Connected with Massive APIs

1 code implementation24 May 2023 Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez

Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis.

Language Modelling Large Language Model +3

Simple Token-Level Confidence Improves Caption Correctness

no code implementations11 May 2023 Suzanne Petryk, Spencer Whitehead, Joseph E. Gonzalez, Trevor Darrell, Anna Rohrbach, Marcus Rohrbach

The ability to judge whether a caption correctly describes an image is a critical part of vision-language understanding.

Image Captioning Language Modelling

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

1 code implementation13 Mar 2023 Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang

As a result, when running OPT-175B on a single 16GB GPU, FlexGen achieves significantly higher throughput compared to state-of-the-art offloading systems, reaching a generation throughput of 1 token/s for the first time with an effective batch size of 144.

Language Modelling Large Language Model

AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving

2 code implementations22 Feb 2023 Zhuohan Li, Lianmin Zheng, Yinmin Zhong, Vincent Liu, Ying Sheng, Xin Jin, Yanping Huang, Zhifeng Chen, Hao Zhang, Joseph E. Gonzalez, Ion Stoica

Model parallelism is conventionally viewed as a method to scale a single large deep learning model beyond the memory limits of a single device.

The Wisdom of Hindsight Makes Language Models Better Instruction Followers

1 code implementation10 Feb 2023 Tianjun Zhang, Fangchen Liu, Justin Wong, Pieter Abbeel, Joseph E. Gonzalez

In this paper, we consider an alternative approach: converting feedback to instruction by relabeling the original one and training the model for better alignment in a supervised manner.

Decision Making Language Modelling +2

Multitask Vision-Language Prompt Tuning

1 code implementation21 Nov 2022 Sheng Shen, Shijia Yang, Tianjun Zhang, Bohan Zhai, Joseph E. Gonzalez, Kurt Keutzer, Trevor Darrell

Specifically, (i) we demonstrate the effectiveness of learning a single transferable prompt from multiple source tasks to initialize the prompt for each target task; (ii) we show many target tasks can benefit each other from sharing prompt vectors and thus can be jointly learned via multitask prompt tuning.

TEMPERA: Test-Time Prompting via Reinforcement Learning

1 code implementation21 Nov 2022 Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, Joseph E. Gonzalez

To achieve this, we design a novel action space that allows flexible editing of the initial prompts covering a wide set of commonly-used components like instructions, few-shot exemplars, and verbalizers.

Few-Shot Learning Natural Language Inference +5

On Optimizing the Communication of Model Parallelism

no code implementations10 Nov 2022 Yonghao Zhuang, Hexu Zhao, Lianmin Zheng, Zhuohan Li, Eric P. Xing, Qirong Ho, Joseph E. Gonzalez, Ion Stoica, Hao Zhang

This pattern emerges when the two paradigms of model parallelism - intra-operator and inter-operator parallelism - are combined to support large models on large clusters.

Using Language to Extend to Unseen Domains

1 code implementation18 Oct 2022 Lisa Dunlap, Clara Mohri, Devin Guillory, Han Zhang, Trevor Darrell, Joseph E. Gonzalez, aditi raghunathan, Anja Rohrbach

It is expensive to collect training data for every possible domain that a vision model may encounter when deployed.

Domain Adaptation

POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging

1 code implementation15 Jul 2022 Shishir G. Patil, Paras Jain, Prabal Dutta, Ion Stoica, Joseph E. Gonzalez

We demonstrate that it is possible to fine-tune both ResNet-18 and BERT within the memory constraints of a Cortex-M class embedded device while outperforming current edge training methods in energy efficiency.

Privacy Preserving

Making Linear MDPs Practical via Contrastive Representation Learning

no code implementations14 Jul 2022 Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai

It is common to address the curse of dimensionality in Markov decision processes (MDPs) by exploiting low-rank representations.

Representation Learning

Neurotoxin: Durable Backdoors in Federated Learning

2 code implementations12 Jun 2022 Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Joseph E. Gonzalez, Kannan Ramchandran, Prateek Mittal

In this type of attack, the goal of the attacker is to use poisoned updates to implant so-called backdoors into the learned model such that, at test time, the model's outputs can be fixed to a given target for certain inputs.

Backdoor Attack Federated Learning

Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data

1 code implementation6 Feb 2022 Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney

Our analyses consider (I) hundreds of Transformers trained in different settings, in which we systematically vary the amount of data, the model size and the optimization hyperparameters, (II) a total of 51 pretrained Transformers from eight families of Huggingface NLP models, including GPT2, BERT, etc., and (III) a total of 28 existing and novel generalization metrics.

Model Selection

Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning

1 code implementation28 Jan 2022 Lianmin Zheng, Zhuohan Li, Hao Zhang, Yonghao Zhuang, Zhifeng Chen, Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica

Existing model-parallel training systems either require users to manually create a parallelization plan or automatically generate one from a limited space of model parallelism configurations.

Representing Long-Range Context for Graph Neural Networks with Global Attention

1 code implementation NeurIPS 2021 Zhanghao Wu, Paras Jain, Matthew A. Wright, Azalia Mirhoseini, Joseph E. Gonzalez, Ion Stoica

Inspired by recent computer vision results that find position-invariant attention performant in learning long-range relationships, our method, which we call GraphTrans, applies a permutation-invariant Transformer module after a standard GNN module.

Graph Classification Graph Embedding

The Effect of Model Size on Worst-Group Generalization

no code implementations8 Dec 2021 Alan Pham, Eunice Chan, Vikranth Srivatsa, Dhruba Ghosh, Yaoqing Yang, Yaodong Yu, Ruiqi Zhong, Joseph E. Gonzalez, Jacob Steinhardt

Overparameterization is shown to result in poor test accuracy on rare subgroups under a variety of settings where subgroup information is known.

Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL

no code implementations NeurIPS 2021 Charles Packer, Pieter Abbeel, Joseph E. Gonzalez

Meta-reinforcement learning (meta-RL) has proven to be a successful framework for leveraging experience from prior tasks to rapidly learn new related tasks, however, current meta-RL approaches struggle to learn in sparse reward environments.

Meta Reinforcement Learning

NovelD: A Simple yet Effective Exploration Criterion

1 code implementation NeurIPS 2021 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

We analyze NovelD thoroughly in MiniGrid and found that empirically it helps the agent explore the environment more uniformly with a focus on exploring beyond the boundary.

Efficient Exploration Montezuma's Revenge +1

Grounded Graph Decoding Improves Compositional Generalization in Question Answering

1 code implementation Findings (EMNLP) 2021 Yu Gai, Paras Jain, Wendi Zhang, Joseph E. Gonzalez, Dawn Song, Ion Stoica

Grounding enables the model to retain syntax information from the input in thereby significantly improving generalization over complex inputs.

Question Answering

C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks

no code implementations ICLR 2022 Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez

Goal-conditioned reinforcement learning (RL) can solve tasks in a wide range of domains, including navigation and manipulation, but learning to reach distant goals remains a central challenge to the field.

Reinforcement Learning (RL)

Taxonomizing local versus global structure in neural network loss landscapes

1 code implementation NeurIPS 2021 Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney

Viewing neural network models in terms of their loss landscapes has a long history in the statistical mechanics approach to learning, and in recent years it has received attention within machine learning proper.

Accelerating Quadratic Optimization with Reinforcement Learning

1 code implementation NeurIPS 2021 Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato, Goran Banjac, Michael Luo, Francesco Borrelli, Joseph E. Gonzalez, Ion Stoica, Ken Goldberg

First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.

reinforcement-learning Reinforcement Learning (RL)

Learning Space Partitions for Path Planning

2 code implementations NeurIPS 2021 Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian

Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function.

CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks

no code implementations14 Jun 2021 Robert Avram, Jeffrey E. Olgin, Alvin Wan, Zeeshan Ahmed, Louis Verreault-Julien, Sean Abreau, Derek Wan, Joseph E. Gonzalez, Derek Y. So, Krishan Soni, Geoffrey H. Tison

Our results demonstrate that multiple purpose-built neural networks can function in sequence to accomplish the complex series of tasks required for automated analysis of real-world angiograms.


PAC Best Arm Identification Under a Deadline

no code implementations6 Jun 2021 Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez

In this work, the decision-maker is given a deadline of $T$ rounds, where, on each round, it can adaptively choose which arms to pull and how many times to pull them; this distinguishes the number of decisions made (i. e., time or number of rounds) from the number of samples acquired (cost).

Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines

no code implementations2 Jun 2021 Matthew A. Wright, Joseph E. Gonzalez

In particular, we show that the "dot-product attention" that is the core of the Transformer's operation can be characterized as a kernel learning method on a pair of Banach spaces.

Deep Attention

Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models

1 code implementation21 Apr 2021 Nicholas Rhinehart, Jeff He, Charles Packer, Matthew A. Wright, Rowan Mcallister, Joseph E. Gonzalez, Sergey Levine

Humans have a remarkable ability to make decisions by accurately reasoning about future events, including the future behaviors and states of mind of other agents.

Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation

no code implementations18 Apr 2021 Ruizhe Cheng, Bichen Wu, Peizhao Zhang, Peter Vajda, Joseph E. Gonzalez

Our model transfers knowledge from pretrained image and sentence encoders and achieves strong performance with only 3M image text pairs, 133x smaller than CLIP.

Zero-Shot Learning

Robust Object Detection via Instance-Level Temporal Cycle Confusion

1 code implementation ICCV 2021 Xin Wang, Thomas E. Huang, Benlin Liu, Fisher Yu, Xiaolong Wang, Joseph E. Gonzalez, Trevor Darrell

Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications.

object-detection Out-of-Distribution Generalization +1

NBDT: Neural-Backed Decision Tree

no code implementations ICLR 2021 Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.

BeBold: Exploration Beyond the Boundary of Explored Regions

2 code implementations15 Dec 2020 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR.

Efficient Exploration NetHack

RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem

1 code implementation NeurIPS 2021 Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika, Joseph E. Gonzalez, Ion Stoica

Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the last few years.

reinforcement-learning Reinforcement Learning (RL)

Untangling Dense Knots by Learning Task-Relevant Keypoints

no code implementations10 Nov 2020 Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan, Jeffrey Ichnowski, Ashwin Balakrishna, Minho Hwang, Vainavi Viswanath, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg

HULK successfully untangles a cable from a dense initial configuration containing up to two overhand and figure-eight knots in 97. 9% of 378 simulation experiments with an average of 12. 1 actions per trial.

Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

2 code implementations29 Oct 2020 Brijen Thananjeyan, Ashwin Balakrishna, Suraj Nair, Michael Luo, Krishnan Srinivasan, Minho Hwang, Joseph E. Gonzalez, Julian Ibarz, Chelsea Finn, Ken Goldberg

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.

reinforcement-learning Reinforcement Learning (RL) +1

A Statistical Framework for Low-bitwidth Training of Deep Neural Networks

1 code implementation NeurIPS 2020 Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez

We show that the FQT gradient is an unbiased estimator of the QAT gradient, and we discuss the impact of gradient quantization on its variance.


Multi-Agent Collaboration via Reward Attribution Decomposition

2 code implementations16 Oct 2020 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

In this work, we propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge and supports ad hoc team play.

Dota 2 Multi-agent Reinforcement Learning +2

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation

1 code implementation1 Sep 2020 Sicheng Zhao, Xiangyu Yue, Shanghang Zhang, Bo Li, Han Zhao, Bichen Wu, Ravi Krishna, Joseph E. Gonzalez, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia, Kurt Keutzer

To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.

Unsupervised Domain Adaptation

Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models

1 code implementation26 Aug 2020 Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E. Gonzalez, Michael W. Mahoney

Replacing BN with the recently-proposed Group Normalization (GN) can reduce gradient diversity and improve test accuracy.

Federated Learning

Boundary thickness and robustness in learning models

1 code implementation NeurIPS 2020 Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney

Using these observations, we show that noise-augmentation on mixup training further increases boundary thickness, thereby combating vulnerability to various forms of adversarial attacks and OOD transforms.

Adversarial Defense Data Augmentation

BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud

no code implementations19 Jun 2020 Mong H. Ng, Kaahan Radia, Jianfei Chen, Dequan Wang, Ionel Gog, Joseph E. Gonzalez

Bird's-eye-view (BEV) is a powerful and widely adopted representation for road scenes that captures surrounding objects and their spatial locations, along with overall context in the scene.

Bird's-Eye View Semantic Segmentation Semantic Segmentation +1

Thompson Sampling for Linearly Constrained Bandits

1 code implementation20 Apr 2020 Vidit Saxena, Joseph E. Gonzalez, Joakim Jaldén

We address multi-armed bandits (MAB) where the objective is to maximize the cumulative reward under a probabilistic linear constraint.

Multi-Armed Bandits Thompson Sampling

VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback

no code implementations19 Apr 2020 Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael. I. Jordan, Ion Stoica

To that end, we first define three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism.

NBDT: Neural-Backed Decision Trees

2 code implementations1 Apr 2020 Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.

Frustratingly Simple Few-Shot Object Detection

5 code implementations ICML 2020 Xin Wang, Thomas E. Huang, Trevor Darrell, Joseph E. Gonzalez, Fisher Yu

Such a simple approach outperforms the meta-learning methods by roughly 2~20 points on current benchmarks and sometimes even doubles the accuracy of the prior methods.

Few-Shot Object Detection Meta-Learning +1

ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions

no code implementations3 Mar 2020 Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Joseph E. Gonzalez, Aaron Ames, Ken Goldberg

Sample-based learning model predictive control (LMPC) strategies have recently attracted attention due to their desirable theoretical properties and their good empirical performance on robotic tasks.

Continuous Control

Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers

2 code implementations26 Feb 2020 Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, Joseph E. Gonzalez

Since hardware resources are limited, the objective of training deep learning models is typically to maximize accuracy subject to the time and memory constraints of training and inference.

Machine Translation Quantization +1

SqueezeWave: Extremely Lightweight Vocoders for On-device Speech Synthesis

1 code implementation16 Jan 2020 Bohan Zhai, Tianren Gao, Flora Xue, Daniel Rothchild, Bichen Wu, Joseph E. Gonzalez, Kurt Keutzer

Automatic speech synthesis is a challenging task that is becoming increasingly important as edge devices begin to interact with users through speech.

Sound Audio and Speech Processing

ANODEV2: A Coupled Neural ODE Framework

1 code implementation NeurIPS 2019 Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E. Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros

It has been observed that residual networks can be viewed as the explicit Euler discretization of an Ordinary Differential Equation (ODE).

Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization

2 code implementations7 Oct 2019 Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Kurt Keutzer, Ion Stoica, Joseph E. Gonzalez

We formalize the problem of trading-off DNN training time and memory requirements as the tensor rematerialization optimization problem, a generalization of prior checkpointing strategies.

Helen: Maliciously Secure Coopetitive Learning for Linear Models

no code implementations16 Jul 2019 Wenting Zheng, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica

Many organizations wish to collaboratively train machine learning models on their combined datasets for a common benefit (e. g., better medical research, or fraud detection).

Fraud Detection

Task-Aware Feature Generation for Zero-Shot Compositional Learning

1 code implementation11 Jun 2019 Xin Wang, Fisher Yu, Trevor Darrell, Joseph E. Gonzalez

In this work, we propose a task-aware feature generation (TFG) framework for compositional learning, which generates features of novel visual concepts by transferring knowledge from previously seen concepts.

Novel Concepts Zero-Shot Learning

Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks

no code implementations31 May 2019 Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan Mcallister, Joseph E. Gonzalez, Sergey Levine, Francesco Borrelli, Ken Goldberg

Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging.

Model-based Reinforcement Learning reinforcement-learning +1

ACE: Adapting to Changing Environments for Semantic Segmentation

no code implementations ICCV 2019 Zuxuan Wu, Xin Wang, Joseph E. Gonzalez, Tom Goldstein, Larry S. Davis

However, neural classifiers are often extremely brittle when confronted with domain shift---changes in the input distribution that occur over time.

Meta-Learning Semantic Segmentation

TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning

1 code implementation CVPR 2019 Xin Wang, Fisher Yu, Ruth Wang, Trevor Darrell, Joseph E. Gonzalez

We show that TAFE-Net is highly effective in generalizing to new tasks or concepts and evaluate the TAFE-Net on a range of benchmarks in zero-shot and few-shot learning.

Few-Shot Learning Zero-Shot Learning

Constrained Thompson Sampling for Wireless Link Optimization

no code implementations28 Feb 2019 Vidit Saxena, Joseph E. Gonzalez, Ion Stoica, Hugo Tullberg, Joakim Jaldén

We model rate selection as a stochastic multi-armed bandit (MAB) problem, where a finite set of transmission rates are modeled as independent bandit arms.

Thompson Sampling

Cloud Programming Simplified: A Berkeley View on Serverless Computing

no code implementations9 Feb 2019 Eric Jonas, Johann Schleier-Smith, Vikram Sreekanti, Chia-Che Tsai, Anurag Khandelwal, Qifan Pu, Vaishaal Shankar, Joao Carreira, Karl Krauth, Neeraja Yadwadkar, Joseph E. Gonzalez, Raluca Ada Popa, Ion Stoica, David A. Patterson

Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud.

Operating Systems

The OoO VLIW JIT Compiler for GPU Inference

no code implementations28 Jan 2019 Paras Jain, Xiangxi Mo, Ajay Jain, Alexey Tumanov, Joseph E. Gonzalez, Ion Stoica

Current trends in Machine Learning~(ML) inference on hardware accelerated devices (e. g., GPUs, TPUs) point to alarmingly low utilization.

Serverless Computing: One Step Forward, Two Steps Back

3 code implementations10 Dec 2018 Joseph M. Hellerstein, Jose Faleiro, Joseph E. Gonzalez, Johann Schleier-Smith, Vikram Sreekanti, Alexey Tumanov, Chenggang Wu

Serverless computing offers the potential to program the cloud in an autoscaling, pay-as-you go manner.

Distributed, Parallel, and Cluster Computing Databases

InferLine: ML Inference Pipeline Composition Framework

1 code implementation5 Dec 2018 Daniel Crankshaw, Gur-Eyal Sela, Corey Zumar, Xiangxi Mo, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov

The dominant cost in production machine learning workloads is not training individual models but serving predictions from increasingly complex prediction pipelines spanning multiple models, machine learning frameworks, and parallel hardware accelerators.

Distributed, Parallel, and Cluster Computing

Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification

no code implementations3 Dec 2018 J. Weston Hughes, Taylor Sittler, Anthony D. Joseph, Jeffrey E. Olgin, Joseph E. Gonzalez, Geoffrey H. Tison

We develop a multi-task convolutional neural network (CNN) to classify multiple diagnoses from 12-lead electrocardiograms (ECGs) using a dataset comprised of over 40, 000 ECGs, with labels derived from cardiologist clinical interpretations.

Classification General Classification

Scaling Video Analytics Systems to Large Camera Deployments

no code implementations7 Sep 2018 Samvit Jain, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Joseph E. Gonzalez

Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical premises.

Tune: A Research Platform for Distributed Model Selection and Training

4 code implementations13 Jul 2018 Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E. Gonzalez, Ion Stoica

We show that this interface meets the requirements for a broad range of hyperparameter search algorithms, allows straightforward scaling of search to large clusters, and simplifies algorithm implementation.

Hyperparameter Optimization Model Selection

Deep Mixture of Experts via Shallow Embedding

no code implementations5 Jun 2018 Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez

Larger networks generally have greater representational power at the cost of increased computational complexity.

Few-Shot Learning Zero-Shot Learning

Fast Semantic Segmentation on Video Using Block Motion-Based Feature Interpolation

no code implementations21 Mar 2018 Samvit Jain, Joseph E. Gonzalez

Convolutional networks optimized for accuracy on challenging, dense prediction tasks are prohibitively slow to run on each frame in a video.

Optical Flow Estimation Semantic Segmentation

Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning

no code implementations28 Feb 2018 Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael. I. Jordan, Joseph E. Gonzalez, Sergey Levine

By enabling wider use of learned dynamics models within a model-free reinforcement learning algorithm, we improve value estimation, which, in turn, reduces the sample complexity of learning.

Continuous Control reinforcement-learning +1

Neural Networks for irregularly observed continuous-time Stochastic Processes

no code implementations ICLR 2018 Francois W. Belletti, Alexander Ku, Joseph E. Gonzalez

Designing neural networks for continuous-time stochastic processes is challenging, especially when observations are made irregularly.

Video Classification

RLlib: Abstractions for Distributed Reinforcement Learning

3 code implementations ICML 2018 Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael. I. Jordan, Ion Stoica

Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation.

reinforcement-learning Reinforcement Learning (RL)

A Berkeley View of Systems Challenges for AI

no code implementations15 Dec 2017 Ion Stoica, Dawn Song, Raluca Ada Popa, David Patterson, Michael W. Mahoney, Randy Katz, Anthony D. Joseph, Michael Jordan, Joseph M. Hellerstein, Joseph E. Gonzalez, Ken Goldberg, Ali Ghodsi, David Culler, Pieter Abbeel

With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production.

Machine Translation speech-recognition +1

SkipNet: Learning Dynamic Routing in Convolutional Networks

2 code implementations ECCV 2018 Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, Joseph E. Gonzalez

While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient.

Decision Making

Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning

no code implementations4 Nov 2017 Richard Liaw, Sanjay Krishnan, Animesh Garg, Daniel Crankshaw, Joseph E. Gonzalez, Ken Goldberg

We explore how Deep Neural Networks can represent meta-policies that switch among a set of previously learned policies, specifically in settings where the dynamics of a new scenario are composed of a mixture of previously learned dynamics and where the state observation is possibly corrupted by sensing noise.

Autonomous Driving reinforcement-learning +1

IDK Cascades: Fast Deep Learning by Learning not to Overthink

no code implementations3 Jun 2017 Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez

Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions.

Dialogue Generation

GraphLab: A New Framework For Parallel Machine Learning

no code implementations9 Aug 2014 Yucheng Low, Joseph E. Gonzalez, Aapo Kyrola, Danny Bickson, Carlos E. Guestrin, Joseph Hellerstein

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging.

BIG-bench Machine Learning

Optimistic Concurrency Control for Distributed Unsupervised Learning

no code implementations NeurIPS 2013 Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael. I. Jordan

Research on distributed machine learning algorithms has focused primarily on one of two extremes - algorithms that obey strict concurrency constraints or algorithms that obey few or no such constraints.

BIG-bench Machine Learning Clustering

Cannot find the paper you are looking for? You can Submit a new open access paper.