Spatial Latent Representations in Generative Adversarial Networks for Image Generation von Sypetkowski, Maciej

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Spatial Latent Representations in Generative Adversarial Networks for Image Generation von Sypetkowski, Maciej

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IMAGE EMBEDDINGS VIA DEEP LEARNING AND ADAPTIVE BATCH NORMALIZATION von SYPETKOWSKI, Maciej, EARNSHAW, Berton Allen

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IMAGE EMBEDDINGS VIA DEEP LEARNING AND ADAPTIVE BATCH NORMALIZATION von SYPETKOWSKI, Maciej, EARNSHAW, Berton Allen

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How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval von Fradkin, Philip, Azadi, Puria, Suri, Karush, Wenkel, Frederik, Bashashati, Ali, Sypetkowski, Maciej, Beaini, Dominique

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Augmentation Inside the Network von Sypetkowski, Maciej, Jasiulewicz, Jakub, Wojna, Zbigniew

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On the Scalability of GNNs for Molecular Graphs von Sypetkowski, Maciej, Wenkel, Frederik, Poursafaei, Farimah, Dickson, Nia, Suri, Karush, Fradkin, Philip, Beaini, Dominique

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Augmentation Inside the Network von Sypetkowski, Maciej, Jasiulewicz, Jakub, Wojna, Zbigniew

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Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology von Kraus, Oren, Kenyon-Dean, Kian, Saberian, Saber, Fallah, Maryam, McLean, Peter, Leung, Jess, Sharma, Vasudev, Khan, Ayla, Balakrishnan, Jia, Celik, Safiye, Beaini, Dominique, Sypetkowski, Maciej, Cheng, Chi Vicky, Morse, Kristen, Makes, Maureen, Mabey, Ben, Earnshaw, Berton

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RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods von Sypetkowski, Maciej, Rezanejad, Morteza, Saberian, Saber, Kraus, Oren, Urbanik, John, Taylor, James, Mabey, Ben, Victors, Mason, Yosinski, Jason, Sereshkeh, Alborz Rezazadeh, Haque, Imran, Earnshaw, Berton

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Masked Autoencoders are Scalable Learners of Cellular Morphology von Kraus, Oren, Kenyon-Dean, Kian, Saberian, Saber, Fallah, Maryam, McLean, Peter, Leung, Jess, Sharma, Vasudev, Khan, Ayla, Balakrishnan, Jia, Celik, Safiye, Sypetkowski, Maciej, Cheng, Chi Vicky, Morse, Kristen, Makes, Maureen, Mabey, Ben, Earnshaw, Berton

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On the Scalability of GNNs for Molecular Graphs von Sypetkowski, Maciej, Wenkel, Frederik, Poursafaei, Farimah, Dickson, Nia, Karush Suri, Fradkin, Philip, Beaini, Dominique

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How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval von Fradkin, Philip, Puria Azadi, Karush Suri, Wenkel, Frederik, Bashashati, Ali, Sypetkowski, Maciej, Beaini, Dominique

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Insights From the NeurIPS 2021 NetHack Challenge von Hambro, Eric, Mohanty, Sharada, Babaev, Dmitrii, Byeon, Minwoo, Chakraborty, Dipam, Grefenstette, Edward, Jiang, Minqi, Jo, Daejin, Kanervisto, Anssi, Kim, Jongmin, Kim, Sungwoong, Kirk, Robert, Kurin, Vitaly, Küttler, Heinrich, Kwon, Taehwon, Lee, Donghoon, Mella, Vegard, Nardelli, Nantas, Nazarov, Ivan, Ovsov, Nikita, Parker-Holder, Jack, Raileanu, Roberta, Ramanauskas, Karolis, Rocktäschel, Tim, Rothermel, Danielle, Samvelyan, Mikayel, Sorokin, Dmitry, Sypetkowski, Maciej, Sypetkowski, Michał

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Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets von Beaini, Dominique, Huang, Shenyang, Cunha, Joao Alex, Li, Zhiyi, Moisescu-Pareja, Gabriela, Dymov, Oleksandr, Maddrell-Mander, Samuel, McLean, Callum, Wenkel, Frederik, Müller, Luis, Mohamud, Jama Hussein, Parviz, Ali, Craig, Michael, Koziarski, Michał, Lu, Jiarui, Zhu, Zhaocheng, Gabellini, Cristian, Klaser, Kerstin, Dean, Josef, Wognum, Cas, Sypetkowski, Maciej, Rabusseau, Guillaume, Rabbany, Reihaneh, Tang, Jian, Morris, Christopher, Koutis, Ioannis, Ravanelli, Mirco, Wolf, Guy, Tossou, Prudencio, Mary, Hadrien, Bois, Therence, Fitzgibbon, Andrew, Banaszewski, Błażej, Martin, Chad, Masters, Dominic

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Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology von Kraus, Oren, Kenyon-Dean, Kian, Saberian, Saber, Fallah, Maryam, McLean, Peter, Leung, Jess, Sharma, Vasudev, Khan, Ayla, Balakrishnan, Jia, Celik, Safiye, Beaini, Dominique, Sypetkowski, Maciej, Chi Vicky Cheng, Morse, Kristen, Makes, Maureen, Mabey, Ben, Earnshaw, Berton

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RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods von Sypetkowski, Maciej, Rezanejad, Morteza, Saberian, Saber, Kraus, Oren, Urbanik, John, Taylor, James, Mabey, Ben, Mason Victors, Yosinski, Jason, Alborz Rezazadeh Sereshkeh, Haque, Imran, Earnshaw, Berton

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Masked Autoencoders are Scalable Learners of Cellular Morphology von Kraus, Oren, Kenyon-Dean, Kian, Saberian, Saber, Fallah, Maryam, McLean, Peter, Leung, Jess, Sharma, Vasudev, Khan, Ayla, Balakrishnan, Jia, Celik, Safiye, Sypetkowski, Maciej, Chi Vicky Cheng, Morse, Kristen, Makes, Maureen, Mabey, Ben, Earnshaw, Berton

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Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings von Beaini, Dominique, Celik, Safiye, Leung, Jessica Wai Yin, Earnshaw, Berton Allen, Saberian, Mohammadsadegh, Khan, Ayla Yasmin, Sharma, Vasudev, Fallah, Maryam, Sypetkowski, Maciej, Kenyon-Dean, Kian Runnels, Mabey, Benjamin John, McLean, Peter Foster, Balakrishnan, Jaichitra, Kraus, Oren Zeev, Morse, Kristen Rose, Cheng, Chi, Makes, Maureen Katherine

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Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings von Beaini, Dominique, Celik, Safiye, Leung, Jessica Wai Yin, Earnshaw, Berton Allen, Saberian, Mohammadsadegh, Khan, Ayla Yasmin, Sharma, Vasudev, Fallah, Maryam, Sypetkowski, Maciej, Kenyon-Dean, Kian Runnels, Mabey, Benjamin John, McLean, Peter Foster, Balakrishnan, Jaichitra, Kraus, Oren Zeev, Morse, Kristen Rose, Cheng, Chi, Makes, Maureen Katherine

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