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Cornell University
,IBM T. J. Watson Research Center
,IBM T. J. Watson Research Center
,Cornell University
Real-world applications often involve domain-specific and task-based performance objectives that are not captured by the standard machine learning losses, but are critical for decision making. A key challenge for direct integration of more meaningful ...
Department of Computer Science, Cornell University
,Department of Computer Science, Cornell University
,School of Computer Science and Informatics, Cardiff University, UK
,Department of Computer Science, Cornell University
,Gulf of Maine Research Institute
,Department of Ecology, Evolution, and Natural Resources, Rutgers University
,Department of Ecology, Evolution, and Natural Resources, Rutgers University
,Gulf of Maine Research Institute
,Department of Computer Science, Cornell University
A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of species have to be simultaneously modeled ...
Department of Computer Science, Cornell University
,Department of Computer Science, Cornell University
,Department of Computer Science, Cornell University
Multi-label classification is the challenging task of predicting the presence and absence of multiple targets, involving representation learning and label correlation modeling. We propose a novel framework for multi-label classification, Multivariate ...
Department of Computer Science, Cornell University
,Department of Computer Science, Cornell University
Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems. Sokoban is a PSPACE-complete planning task and represents one of the hardest domains for current AI planners. Even domain-specific ...
Department of Computer Science, Cornell University, Ithaca, NY
,Department of Computer Science, Cornell University, Ithaca, NY
,Department of Computer Science, Cornell University, Ithaca, NY
In recent years, we have witnessed tremendous progress in deep reinforcement learning (RL) for tasks such as Go, Chess, video games, and robot control. Nevertheless, other combinatorial domains, such as AI planning, still pose considerable challenges for ...
Department of Computer Science, Cornell University, Ithaca, New York
,Department of Computer Science, Cornell University, Ithaca, New York
,Department of Computer Science, Cornell University, Ithaca, New York
,Department of Computer Science, Cornell University, Ithaca, New York
,California Institute of Technology, Pasadena, California
,Department of Computer Science, Cornell University, Ithaca, New York
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with constraint reasoning for solving pattern de-mixing problems, typically in an unsupervised or very-weakly-supervised setting. DRNets exploit problem ...
Cornell University, Ithaca, NY, USA
Artificial Intelligence (AI) is a rapidly advancing field. Novel machine learning methods combined with reasoning and search techniques have led us to reach new milestones: from computer vision, machine translation, and Go and Chess world-champion level ...
Cornell University
,Oregon State University
,Cornell University
,Cornell University
,University of Southern California
,Stanford University
,Carnegie Mellon University
,Cornell University
,Oregon State University
,Oregon State University
,Cornell University
,Vanderbilt University
,Cornell University
,Massachusetts Institute of Technology
,U.S. Geological Survey
,California Institute of Technology
,Cornell University
,Cornell University
,Carnegie Mellon University
,Princeton University
,The Ohio State University
,Oregon State University
,Cornell University
,University of Massachusetts Amherst
,Cornell University
,Harvard University
,Oregon State University
,Cornell University
,Microsoft AI Research
,Purdue University
,Pennsylvania State University
,Howard University
,Bowdoin College
Computer and information scientists join forces with other fields to help solve societal and environmental challenges facing humanity, in pursuit of a sustainable future.
Cornell University
,Purdue University
,Cornell University
Citizen science programs have been instrumental in boosting sustainability projects, large-scale scientific discovery, and crowd-sourced experimentation. Nevertheless, these programs witness challenges in submissions' quality, such as sampling bias ...
Cornell University
,Cornell University
Citizen science projects are successful at gathering rich datasets for various applications. However, the data collected by citizen scientists are often biased — in particular, aligned more with the citizens' preferences than with scientific objectives. ...
Dept. of Computer Science, Cornell University, Ithaca, NY
,Dept. of Computer Science, Cornell University, Ithaca, NY
,Dept. of Computer Science, Cornell University, Ithaca, NY
,Dept. of Computer Science, Cornell University, Ithaca, NY
,Bioacoustics Research Program, Cornell Lab of Ornithology, Ithaca, NY
,Dept. of Computer Science, Cornell University, Ithaca, NY
In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earth's biodiversity is increasingly under threat by sources of anthropogenic change (e.g. ...
University of Southern California
,The University of Texas Austin
,Georgia State University
,University of Minnesota
,Booz Allen Hamilton
,Johns Hopkins University
,Wright State University
,Colorado State University
,Cornell University
,University of Kansas
,University of Utah
,Columbia University
,George Mason University
,University of Southern California
,University of Southern California
,University of Minnesota
,Massachusetts Institute of Technology
,University of Southern California
,Virginia Tech
,Massachusetts Institute of Technology
,University of Wisconsin-Madison
,Indiana University Bloomington
,University of Colorado Boulder
,Massachusetts Institute of Technology
,Oregon State University
,University of Michigan
,University of Maryland
,University of Minnesota
,University of Michigan
,University of California Irvine
,University of Wisconsin-Madison
,National Snow and Ice Data Center
,Carnegie Mellon University
A research agenda for intelligent systems that will result in fundamental new capabilities for understanding the Earth system.
Cornell University
,Cornell University
,Cornell University
,Cornell University
Batch normalization (BN) is a technique to normalize activations in intermediate layers of deep neural networks. Its tendency to improve accuracy and speed up training have established BN as a favorite technique in deep learning. Yet, despite its ...
Center for Applied Mathematics, Cornell University
,Department of Computer Science, Stanford University
,Department of Ecology and Evolutionary, Biology, Cornell University
,U.S. Geological Survey, New York, Cooperative Fish and Wildlife Research Unit, Cornell University
,Department of Ecology and Evolutionary Biology, Cornell University
,Department of Computer Science, Cornell University
We provide an exact and approximation algorithm based on Dynamic Programming and an approximation algorithm based on Mixed Integer Programming for optimizing for the so-called dendritic connectivity on tree-structured networks in a multi-objective ...
Dept of Computer Science, Cornell University
,Dept of Computer Science, Stanford University
,Dept of Computer Science, Cornell University
,Dept of Computer Science, Cornell University
,Dept of Ecology and Evolutionary Biology, Cornell University
,Dept of Earth & Environment, Florida International University
,U.S. Geological Survey, New York Cooperative Fish and Wildlife Unit, Cornell University
,Dept of Biological & Environ. Engr., Cornell University
,Dept of Ecology & Evolutionary Biology, Cornell University
,Dept of Computer Science, Cornell University
Real-world problems are often not fully characterized by a single optimal solution, as they frequently involve multiple competing objectives; it is therefore important to identify the so-called Pareto frontier, which captures solution trade-offs. We ...
Tsinghua University, China
,Cornell University
,Cornell University
,Cornell University
Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme that tightly integrates with deep neural networks and has the ability to ...
Dept. of Computer Science, Cornell University
,Dept. of Computer Science, Shanghai Jiao Tong University
,Dept. of Computer Science, Cornell University
,Dept. of Computer Science, Cornell University
,Dept. of Natural Resources, Cornell University
,Dept. of Computer Science, Cornell University
Cascades represent rapid changes in networks. A cascading phenomenon of ecological and economic impact is the spread of invasive species in geographic landscapes. The most promising management strategy is often biocontrol, which entails introducing a ...
Department of Computer Science, Cornell University, Ithaca, NY
,Department of Computer Science, Cornell University, Ithaca, NY
,Cornell Lab of Ornithology, Ithaca, NY
,Department of Computer Science, Cornell University, Ithaca, NY
,Department of Computer Science, Cornell University, Ithaca, NY
Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that ...
Department of Computer Science, Cornell University, Ithaca, NY
,Department of Computer Science, Cornell University, Ithaca, NY
,Department of Computer Science, Cornell University, Ithaca, NY
,Department of Computer Science, Cornell University, Ithaca, NY
Many network optimization problems can be formulated as stochastic network design problems in which edges are present or absent stochastically. Furthermore, protective actions can guarantee that edges will remain present. We consider the problem of ...
Dept. of Computer Science, Cornell University
,Dept. of Computer Science, Cornell University
,NY Coop. Fish & Wildlife Res. Unit, Dept. of Natural Resources, Cornell University
,College of Computing, Georgia Institute of Technology
,U.S. Geological Survey, NY Coop. Fish & Wildlife Res. Unit, Dept. of Natural Resources, Cornell University
,U.S. Geological Survey, Patuxent Wildlife Research Center
,Dept. of Computer Science, Cornell University
Maintaining landscape connectivity is increasingly important in wildlife conservation, especially for species experiencing the effects of habitat loss and fragmentation. We propose a novel approach to dynamically optimize landscape connectivity. Our ...
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.
Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.
A direct search interface for Author Profiles will be built.
An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.
It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.
The ACM DL is a comprehensive repository of publications from the entire field of computing.
It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.
ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.
Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.
ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.
To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.
Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.
The Service can be applied to all the articles you have ever published with ACM.
Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.
For authors who do not have a free ACM Web Account:
For authors who have an ACM web account, but have not edited their ACM Author Profile page:
For authors who have an account and have already edited their Profile Page:
ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.
Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.
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