1 code implementation • 3 Nov 2023 • Krishu K. Thapa, Bhupinderjeet Singh, Supriya Savalkar, Alan Fern, Kirti Rajagopalan, Ananth Kalyanaraman
Our hypothesis is that attention has a unique ability to capture and exploit correlations that may exist across locations or the temporal spectrum (or both).
no code implementations • 4 Jan 2023 • Aseem Saxena, Paola Pesantez-Cabrera, Rohan Ballapragada, Markus Keller, Alan Fern
Grapevine budbreak is a key phenological stage of seasonal development, which serves as a signal for the onset of active growth.
no code implementations • 21 Sep 2022 • Aseem Saxena, Paola Pesantez-Cabrera, Rohan Ballapragada, Kin-Ho Lam, Markus Keller, Alan Fern
In this paper, we study whether deep learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period.
no code implementations • 4 Jun 2022 • Kin-Ho Lam, Delyar Tabatabai, Jed Irvine, Donald Bertucci, Anita Ruangrotsakun, Minsuk Kahng, Alan Fern
Reinforcement learning (RL) agents are commonly evaluated via their expected value over a distribution of test scenarios.
2 code implementations • 22 May 2022 • Anurag Koul, Mariano Phielipp, Alan Fern
Decision makers often wish to use offline historical data to compare sequential-action policies at various world states.
no code implementations • 9 Apr 2022 • Jeremy Dao, Kevin Green, Helei Duan, Alan Fern, Jonathan Hurst
We show that prior RL policies trained for unloaded locomotion fail for some loads and that simply training in the context of loads is enough to result in successful and improved policies.
no code implementations • 28 Sep 2021 • Kin-Ho Lam, Zhengxian Lin, Jed Irvine, Jonathan Dodge, Zeyad T Shureih, Roli Khanna, Minsuk Kahng, Alan Fern
We describe a user interface and case study, where a small group of AI experts and developers attempt to identify reasoning flaws due to inaccurate agent learning.
no code implementations • 13 Sep 2021 • Li Fuxin, Zhongang Qi, Saeed Khorram, Vivswan Shitole, Prasad Tadepalli, Minsuk Kahng, Alan Fern
This paper summarizes our endeavors in the past few years in terms of explaining image classifiers, with the aim of including negative results and insights we have gained.
2 code implementations • 11 Jul 2021 • Mohamad H Danesh, Alan Fern
This is relevant to applications in control, reinforcement learning (RL), and multi-variate time-series, where changes to test time dynamics can impact the performance of learning controllers/predictors in unknown ways.
1 code implementation • 11 Jun 2021 • Sam Greydanus, Stefan Lee, Alan Fern
Neural networks are a popular tool for modeling sequential data but they generally do not treat time as a continuous variable.
no code implementations • 1 May 2021 • Erich Merrill, Stefan Lee, Li Fuxin, Thomas G. Dietterich, Alan Fern
We consider the problem of modeling the dynamics of continuous spatial-temporal processes represented by irregular samples through both space and time.
no code implementations • 1 Jan 2021 • Samuel James Greydanus, Stefan Lee, Alan Fern
This structure enables our model to jump over long time intervals while retaining the ability to produce fine-grained or continuous-time predictions when necessary.
no code implementations • 14 Dec 2020 • Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa, Alan Fern
We consider the problem of optimizing expensive black-box functions over discrete spaces (e. g., sets, sequences, graphs).
1 code implementation • NeurIPS 2021 • Vivswan Shitole, Li Fuxin, Minsuk Kahng, Prasad Tadepalli, Alan Fern
Attention maps are a popular way of explaining the decisions of convolutional networks for image classification.
no code implementations • 2 Nov 2020 • Jonah Siekmann, Yesh Godse, Alan Fern, Jonathan Hurst
We study the problem of realizing the full spectrum of bipedal locomotion on a real robot with sim-to-real reinforcement learning (RL).
Robotics
1 code implementation • 19 Oct 2020 • Anurag Koul, Varun V. Kumar, Alan Fern, Somdeb Majumdar
Learning and planning with latent space dynamics has been shown to be useful for sample efficiency in model-based reinforcement learning (MBRL) for discrete and continuous control tasks.
2 code implementations • ICLR 2021 • Aayam Shrestha, Stefan Lee, Prasad Tadepalli, Alan Fern
We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience.
1 code implementation • ICLR 2021 • Zhengxian Lin, Kim-Ho Lam, Alan Fern
We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another.
1 code implementation • 6 Jun 2020 • Mohamad H. Danesh, Anurag Koul, Alan Fern, Saeed Khorram
We introduce an approach for understanding control policies represented as recurrent neural networks.
no code implementations • 1 Oct 2019 • Murugeswari Issakkimuthu, Alan Fern, Prasad Tadepalli
There are notable examples of online search improving over hand-coded or learned policies (e. g. AlphaZero) for sequential decision making.
no code implementations • 25 Sep 2019 • Erich Merrill, Alan Fern
In this work, we investigate decoder architectures that more closely match the semantics of variable sized point clouds.
no code implementations • 22 Mar 2019 • Andrew Anderson, Jonathan Dodge, Amrita Sadarangani, Zoe Juozapaitis, Evan Newman, Jed Irvine, Souti Chattopadhyay, Alan Fern, Margaret Burnett
We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents.
no code implementations • 18 Dec 2018 • Mandana Hamidi-Haines, Zhongang Qi, Alan Fern, Fuxin Li, Prasad Tadepalli
For this purpose, we developed a user interface for "interactive naming," which allows a human annotator to manually cluster significant activation maps in a test set into meaningful groups called "visual concepts".
no code implementations • ICLR 2019 • Anurag Koul, Sam Greydanus, Alan Fern
Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems.
1 code implementation • ICML 2018 • Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks
Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates.
3 code implementations • ICML 2018 • Sam Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern
While deep reinforcement learning (deep RL) agents are effective at maximizing rewards, it is often unclear what strategies they use to do so.
no code implementations • 3 Oct 2017 • Jose Picado, Arash Termehchy, Sudhanshu Pathak, Alan Fern, Praveen Ilango, Yunqiao Cai
Relational databases are valuable resources for learning novel and interesting relations and concepts.
2 code implementations • 30 Aug 2017 • Shubhomoy Das, Weng-Keen Wong, Alan Fern, Thomas G. Dietterich, Md Amran Siddiqui
Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective.
no code implementations • CVPR 2017 • Behrooz Mahasseni, Sinisa Todorovic, Alan Fern
In this work, we study a poorly understood trade-off between accuracy and runtime costs for deep semantic video segmentation.
no code implementations • 26 Jul 2016 • Behrooz Mahasseni, Sinisa Todorovic, Alan Fern
Our second contribution is the algorithm for learning a policy for the sparse selection of supervoxels and their descriptors for budgeted CRF inference.
no code implementations • 16 Aug 2015 • Jose Picado, Arash Termehchy, Alan Fern, Parisa Ataei
In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations.
no code implementations • CVPR 2015 • Sheng Chen, Alan Fern, Sinisa Todorovic
This problem is a middle-ground between frame-level person counting, which does not localize counts, and person detection aimed at perfectly localizing people with count-one detections.
1 code implementation • 3 Mar 2015 • Andrew Emmott, Shubhomoy Das, Thomas Dietterich, Alan Fern, Weng-Keen Wong
The intended contributions of this article are many; in addition to providing a large publicly-available corpus of anomaly detection benchmarks, we provide an ontology for describing anomaly detection contexts, a methodology for controlling various aspects of benchmark creation, guidelines for future experimental design and a discussion of the many potential pitfalls of trying to measure success in this field.
no code implementations • 28 Feb 2015 • Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Weng-Keen Wong
An SFE of an anomaly is a sequence of features, which are presented to the analyst one at a time (in order) until the information contained in the highlighted features is enough for the analyst to make a confident judgement about the anomaly.
no code implementations • CVPR 2014 • Sheng Chen, Alan Fern, Sinisa Todorovic
This paper presents a new approach to tracking people in crowded scenes, where people are subject to long-term (partial) occlusions and may assume varying postures and articulations.
no code implementations • 18 Apr 2014 • Robby Goetschalckx, Alan Fern, Prasad Tadepalli
Coactive learning is an online problem solving setting where the solutions provided by a solver are interactively improved by a domain expert, which in turn drives learning.
no code implementations • NeurIPS 2013 • Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli
We address the scalability of symbolic planning under uncertainty with factored states and actions.
no code implementations • NeurIPS 2012 • Aaron Wilson, Alan Fern, Prasad Tadepalli
We consider the problem of learning control policies via trajectory preference queries to an expert.
no code implementations • 27 Jun 2012 • Javad Azimi, Alan Fern, Xiaoli Zhang-Fern, Glencora Borradaile, Brent Heeringa
Most prior work on active learning of classifiers has focused on sequentially selecting one unlabeled example at a time to be labeled in order to reduce the overall labeling effort.
no code implementations • NeurIPS 2011 • Neville Mehta, Prasad Tadepalli, Alan Fern
This paper introduces two new frameworks for learning action models for planning.
no code implementations • NeurIPS 2011 • Javad Azimi, Alan Fern, Xiaoli Z. Fern
This paper defines a novel problem formulation with the following important extensions: 1) allowing for concurrent experiments; 2) allowing for stochastic experiment durations; and 3) placing constraints on both the total number of experiments and the total experimental time.
no code implementations • NeurIPS 2010 • Alan Fern, Prasad Tadepalli
A variation of this policy is shown to achieve worst-case regret that is logarithmic in the number of goals for any goal distribution.
no code implementations • NeurIPS 2010 • Javad Azimi, Alan Fern, Xiaoli Z. Fern
Bayesian optimization methods are often used to optimize unknown functions that are costly to evaluate.