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Article ; Online: WhichTF is functionally important in your open chromatin data?

Tanigawa, Yosuke / Dyer, Ethan S / Bejerano, Gill

2022 Volume 18, Issue 8, Page(s) e1010378

Abstract: We present WhichTF, a computational method to identify functionally important transcription factors (TFs) from chromatin accessibility measurements. To rank TFs, WhichTF applies an ontology-guided functional approach to compute novel enrichment by ... ...

Abstract	We present WhichTF, a computational method to identify functionally important transcription factors (TFs) from chromatin accessibility measurements. To rank TFs, WhichTF applies an ontology-guided functional approach to compute novel enrichment by integrating accessibility measurements, high-confidence pre-computed conservation-aware TF binding sites, and putative gene-regulatory models. Comparison with prior sheer abundance-based methods reveals the unique ability of WhichTF to identify context-specific TFs with functional relevance, including NF-κB family members in lymphocytes and GATA factors in cardiac cells. To distinguish the transcriptional regulatory landscape in closely related samples, we apply differential analysis and demonstrate its utility in lymphocyte, mesoderm developmental, and disease cells. We find suggestive, under-characterized TFs, such as RUNX3 in mesoderm development and GLI1 in systemic lupus erythematosus. We also find TFs known for stress response, suggesting routine experimental caveats that warrant careful consideration. WhichTF yields biological insight into known and novel molecular mechanisms of TF-mediated transcriptional regulation in diverse contexts, including human and mouse cell types, cell fate trajectories, and disease-associated cells.
MeSH term(s)	Animals ; Binding Sites ; Chromatin/genetics ; Gene Expression Regulation ; Humans ; Mice ; Protein Binding ; Transcription Factors/metabolism
Chemical Substances	Chromatin ; Transcription Factors
Language	English
Publishing date	2022-08-30
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1010378
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Asymptotics of Wide Convolutional Neural Networks

Andreassen, Anders / Dyer, Ethan

2020

Abstract: Wide neural networks have proven to be a rich class of architectures for both theory and practice. Motivated by the observation that finite width convolutional networks appear to outperform infinite width networks, we study scaling laws for wide CNNs and ...

Abstract	Wide neural networks have proven to be a rich class of architectures for both theory and practice. Motivated by the observation that finite width convolutional networks appear to outperform infinite width networks, we study scaling laws for wide CNNs and networks with skip connections. Following the approach of (Dyer & Gur-Ari, 2019), we present a simple diagrammatic recipe to derive the asymptotic width dependence for many quantities of interest. These scaling relationships provide a solvable description for the training dynamics of wide convolutional networks. We test these relations across a broad range of architectures. In particular, we find that the difference in performance between finite and infinite width models vanishes at a definite rate with respect to model width. Nonetheless, this relation is consistent with finite width models generalizing either better or worse than their infinite width counterparts, and we provide examples where the relative performance depends on the optimization details. Comment: 23 pages, 12 figures
Keywords	Computer Science - Machine Learning ; High Energy Physics - Theory ; Statistics - Machine Learning
Subject code	612
Publishing date	2020-08-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Anatomy of Catastrophic Forgetting

Ramasesh, Vinay V. / Dyer, Ethan / Raghu, Maithra

Hidden Representations and Task Semantics

2020

Abstract: A central challenge in developing versatile machine learning systems is catastrophic forgetting: a model trained on tasks in sequence will suffer significant performance drops on earlier tasks. Despite the ubiquity of catastrophic forgetting, there is ... ...

Abstract	A central challenge in developing versatile machine learning systems is catastrophic forgetting: a model trained on tasks in sequence will suffer significant performance drops on earlier tasks. Despite the ubiquity of catastrophic forgetting, there is limited understanding of the underlying process and its causes. In this paper, we address this important knowledge gap, investigating how forgetting affects representations in neural network models. Through representational analysis techniques, we find that deeper layers are disproportionately the source of forgetting. Supporting this, a study of methods to mitigate forgetting illustrates that they act to stabilize deeper layers. These insights enable the development of an analytic argument and empirical picture relating the degree of forgetting to representational similarity between tasks. Consistent with this picture, we observe maximal forgetting occurs for task sequences with intermediate similarity. We perform empirical studies on the standard split CIFAR-10 setup and also introduce a novel CIFAR-100 based task approximating realistic input distribution shift.
Keywords	Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Statistics - Machine Learning
Subject code	004 ; 006
Publishing date	2020-07-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: When Do Curricula Work?

Wu, Xiaoxia / Dyer, Ethan / Neyshabur, Behnam

2020

Abstract: Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult ...

Abstract	Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standard i.i.d. training. In this work, we set out to investigate the relative benefits of ordered learning. We first investigate the \emph{implicit curricula} resulting from architectural and optimization bias and find that samples are learned in a highly consistent order. Next, to quantify the benefit of \emph{explicit curricula}, we conduct extensive experiments over thousands of orderings spanning three kinds of learning: curriculum, anti-curriculum, and random-curriculum -- in which the size of the training dataset is dynamically increased over time, but the examples are randomly ordered. We find that for standard benchmark datasets, curricula have only marginal benefits, and that randomly ordered samples perform as well or better than curricula and anti-curricula, suggesting that any benefit is entirely due to the dynamic training set size. Inspired by common use cases of curriculum learning in practice, we investigate the role of limited training time budget and noisy data in the success of curriculum learning. Our experiments demonstrate that curriculum, but not anti-curriculum can indeed improve the performance either with limited training time budget or in existence of noisy data. Comment: ICLR 2021
Keywords	Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing ; Statistics - Machine Learning
Subject code	006
Publishing date	2020-12-05
Publishing country	us
Document type	Book ; Online
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Book ; Online: Block-Recurrent Transformers

Hutchins, DeLesley / Schlag, Imanol / Wu, Yuhuai / Dyer, Ethan / Neyshabur, Behnam

2022

Abstract: We introduce the Block-Recurrent Transformer, which applies a transformer layer in a recurrent fashion along a sequence, and has linear complexity with respect to sequence length. Our recurrent cell operates on blocks of tokens rather than single tokens ... ...

Abstract	We introduce the Block-Recurrent Transformer, which applies a transformer layer in a recurrent fashion along a sequence, and has linear complexity with respect to sequence length. Our recurrent cell operates on blocks of tokens rather than single tokens during training, and leverages parallel computation within a block in order to make efficient use of accelerator hardware. The cell itself is strikingly simple. It is merely a transformer layer: it uses self-attention and cross-attention to efficiently compute a recurrent function over a large set of state vectors and tokens. Our design was inspired in part by LSTM cells, and it uses LSTM-style gates, but it scales the typical LSTM cell up by several orders of magnitude. Our implementation of recurrence has the same cost in both computation time and parameter count as a conventional transformer layer, but offers dramatically improved perplexity in language modeling tasks over very long sequences. Our model out-performs a long-range Transformer XL baseline by a wide margin, while running twice as fast. We demonstrate its effectiveness on PG19 (books), arXiv papers, and GitHub source code. Our code has been released as open source. Comment: Update to NeurIPS camera-ready version
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Neural and Evolutionary Computing
Subject code	006
Publishing date	2022-03-11
Publishing country	us
Document type	Book ; Online
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Article ; Online: Early observations with an ERAS pathway for thyroid and parathyroid surgery: Moving the goalposts forward.

Machado, Nikita / Mortlock, Ryland / Maduka, Richard / Souza Cunha, Ana Eliza / Dyer, Ethan / Long, Anne / Canner, Joseph K / Tanella, Anthony / Gibson, Courtney / Hyman, Jaime / Ogilvie, Jennifer

Surgery

2023 Volume 175, Issue 1, Page(s) 114–120

Abstract: Background: Enhanced recovery after surgery pathways have become the standard of care in various surgical specialties. In this study, we discuss our initial experience with a staged enhanced recovery after surgery pathway in endocrine surgery and assess ...

Abstract	Background: Enhanced recovery after surgery pathways have become the standard of care in various surgical specialties. In this study, we discuss our initial experience with a staged enhanced recovery after surgery pathway in endocrine surgery and assess the impact of this pathway on select perioperative outcomes and unanticipated admissions. Methods: We collected information regarding all thyroid/parathyroid surgeries performed by endocrine surgeons at our institution before and after the implementation of the multi-intervention enhanced recovery after surgery pathway. We compared relevant outcomes for all cases 1 year before (n = 479) and 1 year after (n = 166) implementation of the pathway. We also compared outcomes between enhanced recovery after surgery patient groups with varying levels of enhanced recovery after surgery compliance. Results: Enhanced recovery after surgery was associated with a significant decrease in total length of stay (9.2 vs 7.5 hours, P < .0001). Whereas there was no significant decrease in all-cause unanticipated postoperative admissions, there was a decrease in patient-initiated admissions in the Enhanced recovery after surgery group. There was also a significant decrease in mean postoperative morphine milligram equivalents (14.4 vs 16.2 vs 24.8, P = .0015), average daily morphine milligram equivalents (25.6 vs 45.6 vs 53, P < .0001), and average daily pain scores (1.89 vs 2.38 vs 2.74, P = .0045) in the Enhanced recovery after surgery group (particularly with increasing Enhanced recovery after surgery compliance). There were no significant differences in the requirement for postoperative antiemetics or in the post-anesthesia care unit length of stay. Conclusion: This study demonstrates a significant benefit from Enhanced recovery after surgery pathways for thyroidectomies and parathyroidectomies, even with initial data and a staggered roll-out plan. Further directions include a follow-up study once we reach a higher level of institutional compliance with all components of the Enhanced Recovery After Surgery pathway and a prospective trial to identify the relative significance of different portions of the Enhanced Recovery after Surgery pathway, particularly the superficial cervical plexus block.
MeSH term(s)	Humans ; Analgesics, Opioid ; Length of Stay ; Morphine Derivatives ; Pain, Postoperative ; Postoperative Complications/epidemiology ; Postoperative Complications/prevention & control ; Thyroid Gland
Chemical Substances	Analgesics, Opioid ; Morphine Derivatives
Language	English
Publishing date	2023-11-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	202467-6
ISSN	1532-7361 ; 0039-6060
ISSN (online)	1532-7361
ISSN	0039-6060
DOI	10.1016/j.surg.2023.06.052
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Book ; Online: Affinity and Diversity

Gontijo-Lopes, Raphael / Smullin, Sylvia J. / Cubuk, Ekin D. / Dyer, Ethan

Quantifying Mechanisms of Data Augmentation

2020

Abstract: Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood. In practice, augmentation policies are often chosen using ... ...

Abstract	Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood. In practice, augmentation policies are often chosen using heuristics of either distribution shift or augmentation diversity. Inspired by these, we seek to quantify how data augmentation improves model generalization. To this end, we introduce interpretable and easy-to-compute measures: Affinity and Diversity. We find that augmentation performance is predicted not by either of these alone but by jointly optimizing the two. Comment: 10 pages, 7 figures
Keywords	Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Statistics - Machine Learning
Publishing date	2020-02-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Acute appendicitis severity during the early COVID-19 pandemic period.

Zaikos, Thomas D / Boudiab, Elizabeth M / Peshel, Emanuela C / Wu, Annie A / Dyer, Ethan / Haut, Elliott R / Salimian, Kevan J

Trauma surgery & acute care open

2021 Volume 6, Issue 1, Page(s) e000809

Abstract: Background: The early COVID-19 pandemic period significantly strained the US healthcare system. During this period, consultations and admissions for acute medical conditions decreased, which was associated with an increase in disease-specific morbidity ... ...

Abstract	Background: The early COVID-19 pandemic period significantly strained the US healthcare system. During this period, consultations and admissions for acute medical conditions decreased, which was associated with an increase in disease-specific morbidity and mortality. Therefore, we sought to determine what, if any, effect the early COVID-19 pandemic period had on the presentation, management, and histopathologic severity of acute appendicitis. Methods: We performed a retrospective, observational study to compare the frequencies with which patients presented with acute appendicitis, the proportion of whom were managed surgically, and the distribution of histopathologic disease severity among all resected appendix specimens during the early COVID-19 pandemic period (March 6-June 30, 2020) to equivalent time periods for the 3 preceding/pre-pandemic years (2017-2019). Results: Compared with equivalent pre-pandemic time periods, during the COVID-19 pandemic period there was no significant difference in the number of patients who presented for acute appendicitis, there was a decreased rate of surgical management (81% vs 94%; p=0.014), and there was an overall increase in the incidence of perforated appendicitis (31% vs 16%; p=0.004), including by histopathologic diagnosis (25% vs 11%; p=0.01). Discussion: Despite potential patient hesitancy to present for care, the early COVID-19 pandemic period was associated with no significant change in the number of patients presenting with acute appendicitis; however, there was a significant increase in the incidence of perforated appendicitis. This study highlights the need to encourage patients to avoid late presentation for acute surgical conditions and for the robust planning for the medical management of otherwise surgical abnormalities during episodes of restricted or limited resources. Level of evidence: Level III.
Language	English
Publishing date	2021-08-26
Publishing country	England
Document type	Journal Article
ISSN	2397-5776
ISSN (online)	2397-5776
DOI	10.1136/tsaco-2021-000809
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Article ; Online: Research Techniques Made Simple: Cutaneous Colorimetry: A Reliable Technique for Objective Skin Color Measurement.

Ly, Bao Chau K / Dyer, Ethan B / Feig, Jessica L / Chien, Anna L / Del Bino, Sandra

The Journal of investigative dermatology

2019 Volume 140, Issue 1, Page(s) 3–12.e1

Abstract: Skin color evaluation contributes to assessment of an individual's cutaneous phenotype. Skin color changes provide important clues to disease progression or treatment response. Skin color is also a predictor of skin cancer risk. Melanin pigment, blood ... ...

Abstract	Skin color evaluation contributes to assessment of an individual's cutaneous phenotype. Skin color changes provide important clues to disease progression or treatment response. Skin color is also a predictor of skin cancer risk. Melanin pigment, blood flow, skin thickness, and photoaging contribute to skin color. Melanin, hemoglobin, bilirubin, and carotene are the primary chromophores of skin color. Their concentrations vary depending on the individual's phenotype, anatomic location, external insults of chemical irritants and UVR, and physiological changes. The evaluation and perception of skin color are often subjective. Objective quantification of skin color can be achieved with colorimetric devices such as tristimulus colorimeters. These devices compute the intensity of light reflected from skin and correlate with pigmentation and erythema. Cutaneous color and color changes can be quantified under color organization systems, such as the CIELAB color space, which is standardized by the Commission Internationale de l'Eclairage (CIE). The CIELAB expresses color's lightness, red/green intensity, and yellow/blue intensity, as L, a, and b* values, respectively. Additionally, skin color's full spectral characteristics and cutaneous physiology can be measured with spectrophotometers. This article outlines basic principles of the CIELAB color system and how to optimally use colorimetric devices as a skin research tool.
MeSH term(s)	Colorimetry/instrumentation ; Colorimetry/methods ; Erythema/metabolism ; Erythema/pathology ; Humans ; Melanins/metabolism ; Skin/cytology ; Skin/metabolism ; Skin Neoplasms/metabolism ; Skin Neoplasms/pathology ; Skin Physiological Phenomena ; Skin Pigmentation ; Spectrophotometry ; Ultraviolet Rays
Chemical Substances	Melanins
Language	English
Publishing date	2019-12-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	80136-7
ISSN	1523-1747 ; 0022-202X
ISSN (online)	1523-1747
ISSN	0022-202X
DOI	10.1016/j.jid.2019.11.003
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Book ; Online: The large learning rate phase of deep learning

Lewkowycz, Aitor / Bahri, Yasaman / Dyer, Ethan / Sohl-Dickstein, Jascha / Gur-Ari, Guy

the catapult mechanism

2020

Abstract: The choice of initial learning rate can have a profound effect on the performance of deep networks. We present a class of neural networks with solvable training dynamics, and confirm their predictions empirically in practical deep learning settings. The ... ...

Abstract	The choice of initial learning rate can have a profound effect on the performance of deep networks. We present a class of neural networks with solvable training dynamics, and confirm their predictions empirically in practical deep learning settings. The networks exhibit sharply distinct behaviors at small and large learning rates. The two regimes are separated by a phase transition. In the small learning rate phase, training can be understood using the existing theory of infinitely wide neural networks. At large learning rates the model captures qualitatively distinct phenomena, including the convergence of gradient descent dynamics to flatter minima. One key prediction of our model is a narrow range of large, stable learning rates. We find good agreement between our model's predictions and training dynamics in realistic deep learning settings. Furthermore, we find that the optimal performance in such settings is often found in the large learning rate phase. We believe our results shed light on characteristics of models trained at different learning rates. In particular, they fill a gap between existing wide neural network theory, and the nonlinear, large learning rate, training dynamics relevant to practice. Comment: 25 pages, 19 figures
Keywords	Statistics - Machine Learning ; Computer Science - Machine Learning
Subject code	006
Publishing date	2020-03-04
Publishing country	us
Document type	Book ; Online
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