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WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models

WEDGE: A multi-weather autonomous driving dataset built from...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Marathe, Aboli Ramanan, Deva Walambe, Rahee Kotecha, Ketan Carnegie Mellon University Machine Learning Department PA United States Carnegie Mellon University Robotics Institute PA United States India India

ISBN: (纸本)9798350302493

The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E GEneration): a synthetic dataset generated with a vision-language generative model via prompting. WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection. We have analyzed WEDGE from research standpoints, verifying its effectiveness for extreme-weather autonomous perception. We establish baseline performance for classification and detection with 53.87% test accuracy and 45.41 mAP. Most importantly, WEDGE can be used to fine-tune state-of-the-art detectors, improving SOTA performance on real-world weather benchmarks (such as DAWN) by 4.48 AP for well-generated classes like trucks. WEDGE has been collected under OpenAI's terms 1 of use and is released for public use under the CC BY-NC-SA 4.0 license. The repository for this work and dataset is available at https://***/WEDGE. © 2023 IEEE.

关键词： Benchmarking

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AN EXPERIMENTAL DESIGN PERSPECTIVE ON MODEL-BASED REINFORCEMENT learning 10

AN EXPERIMENTAL DESIGN PERSPECTIVE ON MODEL-BASED REINFORCEM...

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10th International Conference on learning Representations, ICLR 2022

作者： Mehta, Viraj Paria, Biswajit Schneider, Jeff Ermon, Stefano Neiswanger, Willie Robotics Insitute Machine Learning Department Carnegie Mellon University PittsburghPA United States Computer Science Department Stanford University StanfordCA United States

In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or dollars of scientific research. Such expensive data collection prohibits application of standard RL algorithms which usually require a large number of observations to learn. In this work, we address the problem of efficiently learning a policy while making a minimal number of state-action queries to the transition function. In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning. We propose an acquisition function that quantifies how much information a state-action pair would provide about the optimal solution to a Markov decision process. At each iteration, our algorithm maximizes this acquisition function, to choose the most informative state-action pair to be queried, thus yielding a data-efficient RL approach. We experiment with a variety of simulated continuous control problems and show that our approach learns an optimal policy with up to 5-1, 000× less data than model-based RL baselines and 103 - 105× less data than model-free RL baselines. We also provide several ablated comparisons which point to substantial improvements arising from the principled method of obtaining data. © 2022 ICLR 2022 - 10th International Conference on learning Representationss. All rights reserved.

关键词： Markov processes

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∑-optimality for active learning on Gaussian random fields

∑-optimality for active learning on Gaussian random fields

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27th Annual Conference on Neural Information Processing Systems, NIPS 2013

作者： Ma, Yifei Garnett, Roman Schneider, Jeff Machine Learning Department Carnegie Mellon University United States Computer Science Department University of Bonn Germany Robotics Institute Carnegie Mellon University United States

A common classifier for unlabeled nodes on undirected graphs uses label propagation from the labeled nodes, equivalent to the harmonic predictor on Gaussian random fields (GRFs). For active learning on GRFs, the commonly used V-optimality criterion queries nodes that reduce the L2 (regression) loss. V-optimality satisfies a submodularity property showing that greedy reduction produces a (1-1=e) globally optimal solution. However, L2 loss may not characterise the true nature of 0/1 loss in classification problems and thus may not be the best choice for active learning. We consider a new criterion we call ∑-optimality, which queries the node that minimizes the sum of the elements in the predictive covariance. ∑-optimality directly optimizes the risk of the surveying problem, which is to determine the proportion of nodes belonging to one class. In this paper we extend submodularity guarantees from V-optimality to ∑-optimality using properties specific to GRFs. We further show that GRFs satisfy the suppressor-free condition in addition to the conditional independence inherited from Markov random fields. We test ∑-optimality on real-world graphs with both synthetic and real data and show that it outperforms V-optimality and other related methods on classification.

关键词： Gaussian distribution

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Sampling-based multi-dimensional recalibration 24

Sampling-based multi-dimensional recalibration

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Proceedings of the 41st International Conference on machine learning

作者： Youngseog Chung Ian Char Jeff Schneider Machine Learning Department Machine Learning Department and Robotics Institute Carnegie Mellon University Pittsburgh PA

Calibration of probabilistic forecasts in the regression setting has been widely studied in the single dimensional case, where the output variables are assumed to be univariate. In many problem settings, however, the output variables are multi-dimensional, and in the presence of dependence across the output dimensions, measuring calibration and performing recalibration for each dimension separately can be both misleading and detrimental. In this work, we focus on representing predictive uncertainties via samples, and propose a recalibration method which accounts for the joint distribution across output dimensions to produce calibrated samples. Based on the concept of highest density regions (HDR), we define the notion of HDR calibration, and show that our recalibration method produces samples which are HDR calibrated. We demonstrate the performance of our method and the quality of the recalibrated samples on a suite of benchmark datasets in multi-dimensional regression, a real-world dataset in modeling plasma dynamics during nuclear fusion reactions, and on a decision-making application in forecasting demand.

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Mechanisms of Social learning in Evolved Artificial Life

Mechanisms of Social Learning in Evolved Artificial Life

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2020 Conference on Artificial Life, ALIFE 2020

作者： Bartoli, Alberto Catto, Marco De Lorenzo, Andrea Medvet, Eric Talamini, Jacopo Machine Learning Lab. Department of Engineering and Architecture University of Trieste Italy Evolutionary Robotics and Artificial Life Lab. Department of Engineering and Architecture University of Trieste Italy

Adaptation of agents in artificial life scenarios is especially effective when agents may evolve, i.e., inherit traits from their parents, and learn by interacting with the environment. The learning process may be boosted with forms of social learning, i.e., by allowing an agent to learn by combining its experiences with knowledge transferred among agents. In this work, we tackle two specific questions regarding social learning and evolution: (a) from whom learners should learn? (b) how should knowledge be transferred? We address these questions by experimentally investigating two scenarios: a simple one in which the mechanism for evolution and learning is easily interpretable;a more complex and realistic artificial life scenario in which agents compete for survival. Experimental results show that social learning is more profitable when (a) the learners learn from a small set of good teachers and (b) the knowledge to be transferred is determined by teachers experience, rather than learner experience. © 2020 Massachusetts Institute of Technology.

关键词： Adversarial machine learning

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VARIATIONAL AUTOENCODERS IN THE PRESENCE OF LOW-DIMENSIONAL DATA: LANDSCAPE AND IMPLICIT BIAS 10

VARIATIONAL AUTOENCODERS IN THE PRESENCE OF LOW-DIMENSIONAL ...

引用

10th International Conference on learning Representations, ICLR 2022

作者： Koehler, Frederic Mehta, Viraj Zhou, Chenghui Risteski, Andrej Department of Computer Science Stanford University United States Robotics Institute Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

Variational Autoencoders (VAEs) are one of the most commonly used generative models, particularly for image data. A prominent difficulty in training VAEs is data that is supported on a lower dimensional manifold. Recent work by Dai and Wipf (2020) proposes a two-stage training algorithm for VAEs, based on a conjecture that in standard VAE training the generator will converge to a solution with 0 variance which is correctly supported on the ground truth manifold. They gave partial support for this conjecture by showing that some optima of the VAE loss do satisfy this property, but did not analyze the training dynamics. In this paper, we show that for linear encoders/decoders, the conjecture is true-that is the VAE training does recover a generator with support equal to the ground truth manifold-and does so due to an implicit bias of gradient descent rather than merely the VAE loss itself. In the nonlinear case, we show that VAE training frequently learns a higher-dimensional manifold which is a superset of the ground truth manifold. © 2022 ICLR 2022 - 10th International Conference on learning Representationss. All rights reserved.

关键词： Gradient methods

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Communication in Decision Making: Competition favors Inequality

Communication in Decision Making: Competition favors Inequal...

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2020 Conference on Artificial Life, ALIFE 2020

作者： Talamini, Jacopo Medvet, Eric Bartoli, Alberto De Lorenzo, Andrea Machine Learning Lab. Department of Engineering and Architecture University of Trieste Italy Evolutionary Robotics and Artificial Life Lab. Department of Engineering and Architecture University of Trieste Italy

We consider a multi-agent system in which the individual goal is to collect resources, but where the amount of collected resources depends also on others decision. Agents can communicate and can take advantage of being communicated other agents’ plan: therefore they may develop more profitable strategies. We wonder if some kind of collective behaviour, with respect to communication, emerges in this system without being explicitly promoted. To investigate this aspect, we design three different scenarios, respectively a cooperative, a competitive, and a mixed one, in which agents’ behaviors are individually learned by means of reinforcement learning. We consider different strategies concerning communication and learning, including no-communication, always-communication, and optional-communication. Experimental results show that always-communication leads to a collective behaviour with the best results in terms of both overall earned resources and equality between agents. On the other hand optional-communication strategy leads to similar collective strategies in some of these scenarios, but in other scenarios some agents develop individual behaviours that oppose to the collective welfare and thus result in high inequality. © 2020 Massachusetts Institute of Technology.

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A latent space approach to dynamic embedding of co-occurrence data

A latent space approach to dynamic embedding of co-occurrenc...

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11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007

作者： Sarkar, Purnamrita Siddiqi, Sajid M. Gordon, Geoffrey J. Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 United States

We consider dynamic co-occurrence data, such as author-word links in papers published in successive years of the same conference. For static co-occurrence data, researchers often seek an embedding of the entities (authors and words) into a lowdimensional Euclidean space. We generalize a recent static co-occurrence model, the CODE model of Globerson et al. (2004), to the dynamic setting: we seek coordinates for each entity at each time step. The coordinates can change with time to explain new observations, but since large changes are improbable, we can exploit data at previous and subsequent steps to find a better explanation for current observations. To make inference tractable, we show how to approximate our observation model with a Gaussian distribution, allowing the use of a Kalman filter for tractable inference. The result is the first algorithm for dynamic embedding of co-occurrence data which provides distributional information for its coordinate estimates. We demonstrate our model both on synthetic data and on author-word data from the NIPS corpus, showing that it produces intuitively reasonable embeddings. We also provide evidence for the usefulness of our model by its performance on an authorprediction task.

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A reduction of imitation learning and structured prediction to no-regret online learning

A reduction of imitation learning and structured prediction ...

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14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011

作者： Ross, Stéphane Gordon, Geoffrey J. Bagnell, J. Andrew Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 United States Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States

Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches (Daumé III et al., 2009;Ross and Bagnell, 2010) provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem. Copyright 2011 by the authors.

关键词： Stochastic systems

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learning hidden Markov models from non-sequence data via tensor decomposition

Learning hidden Markov models from non-sequence data via ten...

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27th Annual Conference on Neural Information Processing Systems, NIPS 2013

作者： Huang, Tzu-Kuo Schneider, Jeff Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 United States

learning dynamic models from observed data has been a central issue in many scientific studies or engineering tasks. The usual setting is that data are collected sequentially from trajectories of some dynamical system operation. In quite a few modern scientific modeling tasks, however, it turns out that reliable sequential data are rather difficult to gather, whereas out-of-order snapshots are much easier to obtain. Examples include the modeling of galaxies, chronic diseases such Alzheimer's, or certain biological processes. Existing methods for learning dynamic model from non-sequence data are mostly based on Expectation-Maximization, which involves non-convex optimization and is thus hard to analyze. Inspired by recent advances in spectral learning methods, we propose to study this problem from a different perspective: moment matching and spectral decomposition. Under that framework, we identify reasonable assumptions on the generative process of non-sequence data, and propose learning algorithms based on the tensor decomposition method [2] to provably recover firstorder Markov models and hidden Markov models. To the best of our knowledge, this is the first formal guarantee on learning from non-sequence data. Preliminary simulation results confirm our theoretical findings.

关键词： Hidden Markov models

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