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Woodbury matrix identity - Wikipedia
en.wikipedia.org

12 min read, listed 1 hour ago in reddit/MachineLearning/[P] Domain Specific Hyperparameter Optimization for Logistic Regression

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Penalized Regressions: The Bridge versus the Lasso: Journal of Computational and Graphical Statistics: Vol 7, No 3
amstat.tandfonline.com

1 min read, listed 1 hour ago in reddit/MachineLearning/[P] Domain Specific Hyperparameter Optimization for Logistic Regression

(1998). Penalized Regressions: The Bridge versus the Lasso. Journal of Computational and Graphical Statistics: Vol. 7, No. 3, pp. 397-416.

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[2009.09318] Efficient Certification of Spatial Robustness
arxiv.org

1 min read, listed 6 hours ago in reddit/MachineLearning/[R] Efficient Certification of Spatial Robustness

Recent work has exposed the vulnerability of computer vision models to spatial transformations. Due to the widespread usage of such models in safety-critical applications, it is crucial to quantify their robustness against spatial transformations. However, existing work only provides empirical quantification of spatial robustness via adversarial attacks, which lack provable guarantees. In this work, we propose novel convex relaxations, which enable us, for the first time, to provide a certificate of robustness against spatial transformations. Our convex relaxations are model-agnostic and can be leveraged by a wide range of neural network veri...

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[2009.09457] "Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code
arxiv.org

1 min read, listed 7 hours ago in reddit/MachineLearning/[R] "Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code. [TLDR: neural ODEs+small change=train twice as fast]

Neural differential equations may be trained by backpropagating gradients via the adjoint method, which is another differential equation typically solved using an adaptive-step-size numerical differential equation solver. A proposed step is accepted if its error, \emph{relative to some norm}, is sufficiently small; else it is rejected, the step is shrunk, and the process is repeated. Here, we demonstrate that the particular structure of the adjoint equations makes the usual choices of norm (such as $L^2$) unnecessarily stringent. By replacing it with a more appropriate (semi)norm, fewer steps are unnecessarily rejected and the backpropagation...

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[ICRA 2020 Best Paper] Preference-Based Learning for Exoskeleton Gait Optimization - CrossMinds.ai
crossminds.ai

1 min read, listed 11 hours ago in reddit/MachineLearning/[R] Interesting demos of award-winning papers in robotics from ICRA 2020

This paper wins both the ICRA 2020 Best Paper Award and the Best Paper in Human-Robot Interaction Award! This video illustrates preference-based learning for exoskeleton gait optimization. This work appears at ICRA 2020. Additional details can be found in the associated paper: https://arxiv.org/abs/1909.12316 Authors: Tucker, Maegan; Novoseller, Ellen; Kann, Claudia; Sui, Yanan; Yue, Yisong; Burdick, Joel; Ames, Aaron Abstract: This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from u...

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[1909.08605] Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection
arxiv.org

2 min read, listed 11 hours ago in reddit/MachineLearning/[R] Interesting demos of award-winning papers in robotics from ICRA 2020

Semidefinite Programming (SDP) and Sums-of-Squares (SOS) relaxations have led to certifiably optimal non-minimal solvers for several robotics and computer vision problems. However, most non-minimal solvers rely on least-squares formulations, and, as a result, are brittle against outliers. While a standard approach to regain robustness against outliers is to use robust cost functions, the latter typically introduce other non-convexities, preventing the use of existing non-minimal solvers. In this paper, we enable the simultaneous use of non-minimal solvers and robust estimation by providing a general-purpose approach for robust global estimati...

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[ICRA 2020 Best Paper in Cognitive Robotics] Semantic Linking Maps for Active Visual Object Search - ProgressLab @ Michigan - CrossMinds.ai
crossminds.ai

2 min read, listed 11 hours ago in reddit/MachineLearning/[R] Interesting demos of award-winning papers in robotics from ICRA 2020

ICRA 2020 paper: https://7948cefb-1ef7-4c55-96df-fcb8d527c697.filesusr.com/ugd/0886ee_198d7d01f879448bbf00733b21bfcbe9.pdf Result video from "Semantic Linking Maps for Active Visual Object Search" by Z. Zeng, A. Röfer, O.C. Jenkins, ICRA 2020. With the abstract provided below. We aim for mobile robots to function in a variety of common human environments. Such robots need to be able to reason about the locations of previously unseen target objects. Landmark objects can help this reasoning by narrowing down the search space significantly. More specifically, we can exploit background knowledge about common spatial relations between landmark an...

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[1909.12316] Preference-Based Learning for Exoskeleton Gait Optimization
arxiv.org

1 min read, listed 11 hours ago in reddit/MachineLearning/[R] Interesting demos of award-winning papers in robotics from ICRA 2020

This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a protot...

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[ICRA 2020 Best Student Paper & Best Paper in Robot Manipulation] Design of a Roller-Based Dexterous Hand for Object Grasping and Within-Hand Manipulation - CrossMinds.ai
crossminds.ai

1 min read, listed 11 hours ago in reddit/MachineLearning/[R] Interesting demos of award-winning papers in robotics from ICRA 2020

This paper wins both the ICRA 2020 Best Student Paper Award and the Best Paper in Robot Manipulation Award! Paper URL: https://ras.papercept.net/proceedings/ICRA20/1733.pdf Authors: Yuan, Shenli; Epps, Austin; Nowak, Jerome; Salisbury, Kenneth Abstract: This paper describes the development of a novel non-anthropomorphic robot hand with the ability to manipulate objects by means of articulated, actively driven rollers located at the fingertips. An analysis is conducted and systems of equations for two-finger and three-finger manipulation of a sphere are formulated to demonstrate full six degree of freedom nonholonomic spatial motion capabil...

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[ICRA 2020 Best Paper in Robot Vision]: Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection - CrossMinds.ai
crossminds.ai

2 min read, listed 11 hours ago in reddit/MachineLearning/[R] Interesting demos of award-winning papers in robotics from ICRA 2020

ICRA 2020 presentation of the paper "Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection" by Heng Yang, Pasquale Antonante, Vasileios Tzoumas, and Luca Carlone. Arxiv preprint: https://arxiv.org/abs/1909.08605 Abstract: Semidefinite Programming (SDP) and Sums-of-Squares (SOS) relaxations have led to certifiably optimal non-minimal solvers for several robotics and computer vision problems. However, most non-minimal solvers rely on least squares formulations, and, as a result, are brittle against outliers. While a standard approach to regain robustness against outliers is to use robust c...

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[2009.08576] Pruning Neural Networks at Initialization: Why are We Missing the Mark?
arxiv.org

1 min read, listed 12 hours ago in reddit/MachineLearning/[R] Pruning Neural Networks at Initialization: Why are We Missing the Mark?

Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et al., 2019), GraSP (Wang et al., 2020), SynFlow (Tanaka et al., 2020), and magnitude pruning. Although these methods surpass the trivial baseline of random pruning, they remain below the accuracy of magnitude pruning after training, and we endeavor to understand why. We show that, unlike pruning after training, accuracy is the same or higher when randomly shuffling which weights these methods prune within each layer or sampling new initial values. As such, the per-weight pruning decisions made by these methods c...

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Pruning Neural Networks at Initialization: Why are We Missing the Mark? – arXiv Vanity
www.arxiv-vanity.com

18 min read, listed 12 hours ago in reddit/MachineLearning/[R] Pruning Neural Networks at Initialization: Why are We Missing the Mark?

Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et al., 2019), GraSP (Wang et al., 2020), SynFlow (Tanaka et al., 2020), and magnitude pruning. Although these methods surpass the trivial baseline of random pruning, they remain below the accuracy of magnitude pruning after training, and we endeavor to understand why. We show that, unlike pruning after training, accuracy is the same or higher when randomly shuffling which weights these methods prune within each layer or sampling new initial values. As such, the per-weight pruning decisions made by these methods c...

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[2009.02252] KILT: a Benchmark for Knowledge Intensive Language Tasks
arxiv.org

1 min read, listed 13 hours ago in reddit/LanguageTechnology/Facebook AI Releases KILT, A New Benchmark For Knowledge-Intensive NLP Tasks

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Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom knowledge sources, in addition to dedicated infrastructure. To catalyze research on models that condition on specific information in large textual resources, we present a benchmark for knowledge-intensive language tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia, reducing engineering turnaround throu...

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DiffWave: A Versatile Diffusion Model for Audio Synthesis – arXiv Vanity
www.arxiv-vanity.com

1 min read, listed 16 hours ago in reddit/MachineLearning/[R] DiffWave: A Versatile Diffusion Model for Audio Synthesis

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[2009.09761] DiffWave: A Versatile Diffusion Model for Audio Synthesis
arxiv.org

1 min read, listed 16 hours ago in reddit/MachineLearning/[R] DiffWave: A Versatile Diffusion Model for Audio Synthesis

In this work, we propose DiffWave, a versatile Diffusion probabilistic model for conditional and unconditional Waveform generation. The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained by optimizing a variant of variational bound on the data likelihood. DiffWave produces high-fidelity audios in Different Waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation. We demonstrate that DiffWave matches a strong WaveNet vocoder i...

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Toward neuroprosthetic real-time communication from in silico to biological neuronal network via patterned optogenetic stimulation | Scientific Reports
www.nature.com

14 min read, listed 18 hours ago in reddit/singularity/Researchers have developed a neural signal analysis system with memristor arrays, paving the way for high-efficiency brain-machine interfaces, according to Tsinghua University. Power consumption of the system is less than one four-hundredth of that of conventional neural signal analysis systems

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Google AI Blog: Advancing NLP with Efficient Projection-Based Model Architectures
ai.googleblog.com

6 min read, listed 23 hours ago in Google AI Blog

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Posted by Prabhu Kaliamoorthi, Software Engineer, Google Research Deep neural networks have radically transformed natural language proces...

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[2005.13407] CausaLM: Causal Model Explanation Through Counterfactual Language Models
arxiv.org

1 min read, listed 1 day ago in reddit/MachineLearning/[D] Yet another paper to show how BERT relies on easy clues instead of language understanding

Understanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all ML-based methods, they are as good as their training data, and can also capture unwanted biases. While there are tools that can help understand whether such biases exist, they do not distinguish between correlation and causation, and might be ill-suited for text-based models and for reasoning about high level language concepts. A key problem of estimating the causal effect of a concept of interest on a given model is that this estimation requires the generation of counterfactual examples, which is challenging with e...

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[2009.08590] NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative
arxiv.org

1 min read, listed 1 day ago in reddit/MachineLearning/[D] Yet another paper to show how BERT relies on easy clues instead of language understanding

Millions of people around the world are sharing COVID-19 related information on social media platforms. Since not all the information shared on the social media is useful, a machine learning system to identify informative posts can help users in finding relevant information. In this paper, we present a BERT classifier system for W-NUT2020 Shared Task 2: Identification of Informative COVID-19 English Tweets. Further, we show that BERT exploits some easy signals to identify informative tweets, and adding simple patterns to uninformative tweets drastically degrades BERT performance. In particular, simply adding 10 deaths to tweets in dev set, re...

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[2009.05524] Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
arxiv.org

2 min read, listed 1 day ago in Import AI 215: The Hardware Lottery; micro GPT3; and, the Peace Computer

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Recent work in deep reinforcement learning (RL) has produced algorithms capable of mastering challenging games such as Go, chess, or shogi. In these works the RL agent directly observes the natural state of the game and controls that state directly with its actions. However, when humans play such games, they do not just reason about the moves but also interact with their physical environment. They understand the state of the game by looking at the physical board in front of them and modify it by manipulating pieces using touch and fine-grained motor control. Mastering complicated physical systems with abstract goals is a central challenge for...

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[2009.07262] Report prepared by the Montreal AI Ethics Institute (MAIEI) for Publication Norms for Responsible AI by Partnership on AI
arxiv.org

2 min read, listed 1 day ago in Import AI 215: The Hardware Lottery; micro GPT3; and, the Peace Computer

1

The history of science and technology shows that seemingly innocuous developments in scientific theories and research have enabled real-world applications with significant negative consequences for humanity. In order to ensure that the science and technology of AI is developed in a humane manner, we must develop research publication norms that are informed by our growing understanding of AI's potential threats and use cases. Unfortunately, it's difficult to create a set of publication norms for responsible AI because the field of AI is currently fragmented in terms of how this technology is researched, developed, funded, etc. To examine this ...

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[2009.07133] A Mobile App for Wound Localization using Deep Learning
arxiv.org

1 min read, listed 1 day ago in Import AI 215: The Hardware Lottery; micro GPT3; and, the Peace Computer

We present an automated wound localizer from 2D wound and ulcer images by using deep neural network, as the first step towards building an automated and complete wound diagnostic system. The wound localizer has been developed by using YOLOv3 model, which is then turned into an iOS mobile application. The developed localizer can detect the wound and its surrounding tissues and isolate the localized wounded region from images, which would be very helpful for future processing such as wound segmentation and classification due to the removal of unnecessary regions from wound images. For Mobile App development with video processing, a lighter vers...

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[2008.02397] DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data
arxiv.org

1 min read, listed 1 day ago in reddit/MachineLearning/[R] Dimension-Adaptive Neural Architecture for Multivariate Temporal Data

Current deep neural architectures for processing sensor data are mainly designed for data coming from a fixed set of sensors, with a fixed sampling rate. Changing the dimensions of the input data causes considerable accuracy loss, unnecessary computations, or application failures. To address this problem, we introduce a {\em dimension-adaptive pooling}~(DAP) layer that makes deep architectures robust to temporal changes in sampling rate and in sensor availability. DAP operates on convolutional filter maps of variable dimensions and produces an input of fixed dimensions suitable for feedforward and recurrent layers. Building on this architectu...

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GitHub - mmalekzadeh/dana: Dimension-Adaptive Neural Architecture (DANA)
github.com

1 min read, listed 1 day ago in reddit/MachineLearning/[R] Dimension-Adaptive Neural Architecture for Multivariate Temporal Data

Dimension-Adaptive Neural Architecture (DANA). Contribute to mmalekzadeh/dana development by creating an account on GitHub.

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Vector autoregression - Wikipedia
en.wikipedia.org

15 min read, listed 1 day ago in reddit/MachineLearning/[D] Non DL Techniques for multivariate time series prediction

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