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Three scenarios for continual learning [article]

Gido M. van de Ven, Andreas S. Tolias
<span title="2019-04-15">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
{p c 1 , ..., p c m } for each new class c.  ...  Reduce exemplar-sets for old classes If the existing exemplar-sets for the old classes contain more than m exemplars each, for each old class the last selected exemplars are discarded until only m exemplars  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.07734v1">arXiv:1904.07734v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r7nvzhzt2nd27bucsm7uopwwga">fatcat:r7nvzhzt2nd27bucsm7uopwwga</a> </span>
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Class-Incremental Learning with Generative Classifiers [article]

Gido M. van de Ven, Zhe Li, Andreas S. Tolias
<span title="2021-04-28">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This research project has been supported by the Lifelong Learning Machines (L2M) program of the De-  ...  IEEE, 2019. 3 [30] Shuang Li, Yilun Du, Gido M van de Ven, Antonio Tor-David Lopez-Paz and Marc'Aurelio Ranzato. Gradient episodic memory for continual learning.  ...  de Weijer.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.10093v2">arXiv:2104.10093v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ja3wqzaqh5asnai4iz37v2iiha">fatcat:ja3wqzaqh5asnai4iz37v2iiha</a> </span>
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Energy-Based Models for Continual Learning [article]

Shuang Li, Yilun Du, Gido M. van de Ven, Igor Mordatch
<span title="2021-02-18">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
van de Ven, G. M. and Tolias, A. S. Three scenarios for continual learning. arXiv preprint arXiv:1904.07734, 2019. van de Ven, G. M., Siegelmann, H. T., and Tolias, A. S.  ...  Following (van de Ven et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.12216v2">arXiv:2011.12216v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vnn4ahwa6nbd3l2cu24vlo43me">fatcat:vnn4ahwa6nbd3l2cu24vlo43me</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210220020240/https://arxiv.org/pdf/2011.12216v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9f/8d/9f8df3c339079f8d2612f48bd0db22163999fccf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.12216v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Brain-inspired replay for continual learning with artificial neural networks

Gido M. van de Ven, Hava T. Siegelmann, Andreas S. Tolias
<span title="2020-08-13">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/a4wan6l5o5dfzn767kyz7jqevi" style="color: black;">Nature Communications</a> </i> &nbsp;
This intuition Sample z a M G x (t ) (x (1) ,y (1) ) M G ... b Task 1 (x (2) ,y (2) ) M G Task 2 ( ) (x (3) ,y (3) ) M G Task 3 xˆˆŷ (t ) ŷ (2) , (2) x y (1) , (1) x y (1) , (1)  ...  ; as generator (see a Softmax Model ' x e t r o 'C Data storage s u p m c p i a o p 'H b Softmax Main model 'C o r t e x ' z Generator 'H i p p o c a p s ' u m ' Input Input Input New data Exact replay  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41467-020-17866-2">doi:10.1038/s41467-020-17866-2</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32792531">pmid:32792531</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7426273/">pmcid:PMC7426273</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ndod2rwhjzg3dbradsb35b7z6e">fatcat:ndod2rwhjzg3dbradsb35b7z6e</a> </span>
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Generative replay with feedback connections as a general strategy for continual learning [article]

Gido M. van de Ven, Andreas S. Tolias
<span title="2019-04-17">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A major obstacle to developing artificial intelligence applications capable of true lifelong learning is that artificial neural networks quickly or catastrophically forget previously learned tasks when trained on a new one. Numerous methods for alleviating catastrophic forgetting are currently being proposed, but differences in evaluation protocols make it difficult to directly compare their performance. To enable more meaningful comparisons, here we identified three distinct scenarios for
more &raquo; ... nual learning based on whether task identity is known and, if it is not, whether it needs to be inferred. Performing the split and permuted MNIST task protocols according to each of these scenarios, we found that regularization-based approaches (e.g., elastic weight consolidation) failed when task identity needed to be inferred. In contrast, generative replay combined with distillation (i.e., using class probabilities as "soft targets") achieved superior performance in all three scenarios. Addressing the issue of efficiency, we reduced the computational cost of generative replay by integrating the generative model into the main model by equipping it with generative feedback or backward connections. This Replay-through-Feedback approach substantially shortened training time with no or negligible loss in performance. We believe this to be an important first step towards making the powerful technique of generative replay scalable to real-world continual learning applications.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1809.10635v2">arXiv:1809.10635v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/76fmjic7gzgj3lkyg5vffhgpui">fatcat:76fmjic7gzgj3lkyg5vffhgpui</a> </span>
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Hippocampal Offline Reactivation Consolidates Recently Formed Cell Assembly Patterns during Sharp Wave-Ripples

Gido M. van de Ven, Stéphanie Trouche, Colin G. McNamara, Kevin Allen, David Dupret
<span title="">2016</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5lze265mrrcddlccx7zpfjyjwi" style="color: black;">Neuron</a> </i> &nbsp;
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuron.2016.10.020">doi:10.1016/j.neuron.2016.10.020</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27840002">pmid:27840002</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5158132/">pmcid:PMC5158132</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vvizrxwfzjcxtgec5ns36g22hu">fatcat:vvizrxwfzjcxtgec5ns36g22hu</a> </span>
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Natural continual learning: success is a journey, not (just) a destination [article]

Ta-Chu Kao, Kristopher T. Jensen, Gido M. van de Ven, Alberto Bernacchia, Guillaume Hennequin
<span title="2021-12-15">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
., 2020; van de Ven and Tolias, 2019) .  ...  For each benchmark, we considered three continual learning settings (van de Ven and Tolias, 2019).  ...  Algorithm 1: NCL with momentum 1 input: f (network), {D k } K k=1 , α, p w (prior), B (batch size), γ (learning rate), θ 0 , ρ 2 initialize: A θ ← p w I, G θ ← p w I, 3 initialize: θ 1 ← θ 0 , initialize: M  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.08085v2">arXiv:2106.08085v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/z25zqq37drgfbmm3jmdomymkjm">fatcat:z25zqq37drgfbmm3jmdomymkjm</a> </span>
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Parsing Hippocampal Theta Oscillations by Nested Spectral Components during Spatial Exploration and Memory-Guided Behavior

Vítor Lopes-dos-Santos, Gido M. van de Ven, Alexander Morley, Stéphanie Trouche, Natalia Campo-Urriza, David Dupret
<span title="2018-10-18">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5lze265mrrcddlccx7zpfjyjwi" style="color: black;">Neuron</a> </i> &nbsp;
de Ven et al., 2016) .  ...  Psychol. 4, 257-275.van de Ven, G.M., Trouche, S., McNamara, C.G., Allen, K., and Dupret, D. (2016).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuron.2018.09.031">doi:10.1016/j.neuron.2018.09.031</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30344040">pmid:30344040</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6277817/">pmcid:PMC6277817</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/32aa5idgb5e4feajvbp3wiifbe">fatcat:32aa5idgb5e4feajvbp3wiifbe</a> </span>
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Recoding a cocaine-place memory engram to a neutral engram in the hippocampus

Stéphanie Trouche, Pavel V Perestenko, Gido M van de Ven, Claire T Bratley, Colin G McNamara, Natalia Campo-Urriza, S Lucas Black, Leon G Reijmers, David Dupret
<span title="2016-02-22">2016</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lnbpt3q37zbs5nc5hp4wlttdwy" style="color: black;">Nature Neuroscience</a> </i> &nbsp;
Many people assume that addiction is not a disease but a weakness of character. 1 This misconception contributes to the stigma of addiction and unfairly minimizes the challenge of overcoming chemical dependence. Advances in neuroscience and imaging technology have rapidly evolved our understanding of addiction and demonstrated a great deal of support for what is often referred to as the brain disease model of addiction. This model considers genetic and environmental factors that cause physical
more &raquo; ... hanges to the brain and is the basis for many existing and emerging concepts, including (but not limited to) genetic predisposition to addiction, behavioral addictions, psychopharmacological treatment interventions, and cross-addiction. Defining Addiction The complexity of addiction makes it difficult to define in a way that allows for precise study. Due to the significant impacts of social, developmental, and other external factors on addiction, symptoms and prognoses vary a great deal from person to person. This interdisciplinary complexity has drawn comparisons between addiction and other complex diseases, including obesity, diabetes, and cardiovascular disease. 2, 3 However, most scientists agree that a number of characteristics are generally present among addicted individuals. In summarizing the disease model of addiction, Volkow, Koob, and McLellan (2016) highlighted three primary symptoms of addiction: (1) desensitization of the reward circuits of the brain; (2) increased conditioned responses related to the substance an individual is dependent upon; and, (3) declining function of brain regions that facilitate decision making and selfregulation. These themes are echoed throughout the neurobiological literature on addiction 2, 4 and are also common in other psychology fields, including behavioral conditioning 5 ,6 and behavioral economics research. 7
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/nn.4250">doi:10.1038/nn.4250</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26900924">pmid:26900924</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4817230/">pmcid:PMC4817230</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pifaymuzijditgq5w73m3blgu4">fatcat:pifaymuzijditgq5w73m3blgu4</a> </span>
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Representation Ensembling for Synergistic Lifelong Learning with Quasilinear Complexity [article]

Joshua T. Vogelstein, Jayanta Dey, Hayden S. Helm, Will LeVine, Ronak D. Mehta, Ali Geisa, Haoyin Xu, Gido M. van de Ven, Chenyu Gao, Weiwei Yang, Bryan Tower, Jonathan Larson (+2 others)
<span title="2022-05-14">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In practice, we use the architecture described in van de Ven et al.  ...  [25] , van de Ven et al. [26] showed if one algorithm is allowed to keep the old task data, one can mitigate catastrophic forgetting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.12908v13">arXiv:2004.12908v13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6xjkgr3u5fdgfjxs4cteldrnau">fatcat:6xjkgr3u5fdgfjxs4cteldrnau</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220524020844/https://arxiv.org/pdf/2004.12908v13.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/93/dd/93dd8e707728b3b56399a7d83c099f1070f24e0d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.12908v13" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Management and Ecology of Lake and Reservoir Fisheries

Keith B. Gido
<span title="">2003</span> <i title="American Society of Agronomy"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oz4y7ttigbgolliy33poz6rlay" style="color: black;">Journal of Environmental Quality</a> </i> &nbsp;
Chapters by van Zyll de Jong et al. and Turner et al. provide excellent examples of using modern technological advances (e.g., computer modeling and molecular techniques) to address management issues.  ...  GIDO Division of Biology Kansas State University Manhattan, KS 66506 (kgido@ksu.edu)  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2134/jeq2003.1150">doi:10.2134/jeq2003.1150</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fw35t64itnccjovniurf4rr5lq">fatcat:fw35t64itnccjovniurf4rr5lq</a> </span>
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Page 557 of American Lawyer Vol. 5, Issue 11 [page]

<span title="">1897</span> <i title="Open Court Publishing Co"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_american-lawyer-1893" style="color: black;">American Lawyer </a> </i> &nbsp;
M Bailey's Refer to Industrial ‘Trust Co. Manufacturers Seen ee Hardware Co. Bank and Atlantic National Bank. CONARD 4 Gido TON, 435 Chestnut st. Attor- FRANK H.  ...  McNair hia) Busitices established Commercial, insurance law and col- Collection de apetuene thoroughly References : Stet on cand & FRANCISCUS Provident Bldg. Commercial m law.  ... 
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Automatic Recall Machines: Internal Replay, Continual Learning and the Brain [article]

Xu Ji, Joao Henriques, Tinne Tuytelaars, Andrea Vedaldi
<span title="2020-12-12">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In Advances in Neural Information Processing Systems, pages 2990-2999, 2017.Gido M van de Ven, Hava T Siegelmann, and Andreas S Tolias.  ...  Gido M van de Ven and Andreas S Tolias. Generative replay with feedback connections as a general strategy for continual learning. arXiv preprint arXiv:1809.10635, 2018.  ... 
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Orchidaceae del Hato Piñero, Estado Cojedes, Venezuela

Francisco
<span title="2021-04-09">2021</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Se incluye una clave para la identificación de las especies, con notas sobre su tiempo de floración, distribución y etimología de los nombres específicos.  ...  Se citan y describen las Orchidaceae conocidas en el Hato Piñero, en la región de los Llanos Centrales de la cuenca del Orinoco, representadas por 22 géneros y 29 especies; 13 constituyen nuevas adiciones  ...  Sus cotas altitudinales van desde los 50 a los 250 m s.n.m. (Figura 1).  ... 
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Crecimiento y ciclo reproductivo de Polymesoda radiata (Bivalvia: Corbiculidae) en Costa Rica

Eleazar Ruiz Campos, Jorge Cabrera Peña, Rafael A Cruz, José A Palacios
<span title="2015-07-23">2015</span> <i title="Universidad de Costa Rica"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7c4p3xemuzem5nqt3eo3d73fwa" style="color: black;">Revista de Biología Tropical</a> </i> &nbsp;
f:\ ;' M • : S�sedi!  ...  Desovados: En las hembras los lóbulos se ven vacíos con pocos óvulos redondeados. En los machos se ven pocos espermatozoides hacia el centro del lumen.  ... 
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