Fetching the paper…
Reading the bibliography…
Deep neural networks (DNNs) have shown much empirical success in solving perceptual tasks across various cognitive modalities.
Linguistic knowledge and transferability of contextual representations
Liu, N. F., Gardner, M., Belinkov, Y., Peters, M. E., and Smith, N. A · 1903
Earlier work this paper cites.
Bert rediscovers the classical nlp pipeline
Tenney, I., Das, D., and Pavlick, E · 1905
Earlier work this paper cites.
Tenney, I., Xia, P., Chen, B., Wang, A., Poliak, A., McCoy, R. T., Kim, N., Van Durme, B., Bowman, S. R., Das, D., et al · 1905
Earlier work this paper cites.
Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition
Cover, T. M · 1965
Earlier work this paper cites.
Semantics (vol. 1 & vol. 2), 1977
Lyons, J · 1977
Earlier work this paper cites.
The space of interactions in neural network models
Gardner, E. J · 1988
Earlier work this paper cites.
Building a large annotated corpus of English: The Penn Treebank
Marcus, M. P., Santorini, B., and Marcinkiewicz, M. A · 2004
Earlier work this paper cites.
Ontonotes: A large training corpus for enhanced processing
Weischedel, R., Hovy, E., Marcus, M., Palmer, M., Belvin, R., Pradhan, S., Ramshaw, L., and Xue, N · 2011
Earlier work this paper cites.
Representational geometry: integrating cognition, computation, and the brain
Kriegeskorte, N. and Kievit, R. A · 2013
Earlier work this paper cites.
Deep supervised, but not unsupervised, models may explain it cortical representation
Khaligh-Razavi, S.-M. and Kriegeskorte, N · 2014
Earlier work this paper cites.
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., and DiCarlo, J. J · 2014
Earlier work this paper cites.
Geodesics of learned representations
Hénaff, O. J. and Simoncelli, E. P · 2015
Earlier work this paper cites.
Deep neural networks: a new framework for modeling biological vision and brain information processing
Kriegeskorte, N · 2015
Earlier work this paper cites.
Semantic tagging with deep residual networks
Bjerva, J., Plank, B., and Bos, J · 2016
Earlier work this paper cites.
Towards universal semantic tagging
Abzianidze, L. and Bos, J · 2017
Cited alongside, same era.
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Raghu, M., Gilmer, J., Yosinski, J., and Sohl-Dickstein, J · 2017
Cited alongside, same era.
Attention Is All You Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Cited alongside, same era.
Deep rnns encode soft hierarchical syntax
Blevins, T., Levy, O., and Zettlemoyer, L · 2018
Cited alongside, same era.
Classification and geometry of general perceptual manifolds
Chung, S., Lee, D. D., and Sompolinsky, H · 2018
Cited alongside, same era.
Visualizing and Measuring the Geometry of BERT
Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viegas, F., and Wattenberg, M · 2019
Later among the works it cites.
Separability and geometry of object manifolds in deep neural networks
Cohen, U., Chung, S., Lee, D. D., and Sompolinsky, H · 2019
Later among the works it cites.
Weight agnostic neural networks
Gaier, A. and Ha, D · 2019
Later among the works it cites.
Perceptual straightening of natural videos
Hénaff, O. J., Goris, R. L., and Simoncelli, E. P · 2019
Later among the works it cites.
Designing and Interpreting Probes with Control Tasks
Hewitt, J. and Liang, P · 2019
Later among the works it cites.
A Structural Probe for Finding Syntax in Word Representations
Hewitt, J. and Manning, C. D · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Devlin, J., Chang, M.-W., Lee, K., , and Toutanova, K · 2018
Cited alongside, same era.
Empirical study of the topology and geometry of deep networks
Fawzi, A., Moosavi-Dezfooli, S.-M., Frossard, P., and Soatto, S · 2018
Cited alongside, same era.
Deep contextualized word representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I · 2018
Cited alongside, same era.
Zhang, K. W. and Bowman, S. R · 2018
Cited alongside, same era.
Intrinsic dimension of data representations in deep neural networks
Ansuini, A., Laio, A., Macke, J. H., and Zoccolan, D · 2019
Cited alongside, same era.
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
Barrett, D. G., Morcos, A. S., and Macke, J. H · 2019
Cited alongside, same era.
What does bert learn about the structure of language?
Jawahar, G., Sagot, B., and Djame, S · 2019
Later among the works it cites.
Albert: A lite bert for self-supervised learning of language representations, 2019
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R · 2019
Later among the works it cites.
Dimensionality compression and expansion in deep neural networks
Recanatesi, S., Farrell, M., Advani, M., Moore, T., Lajoie, G., and Shea-Brown, E · 2019
Later among the works it cites.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, 2019
Sanh, V., Debut, L., Chaumond, J., and Wolf, T · 2019
Later among the works it cites.
Untangling in invariant speech recognition
Stephenson, C., Feather, J., Padhy, S., Elibol, O., Tang, H., McDermott, J., and Chung, S · 2019
Later among the works it cites.
The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives
Voita, E., Sennrich, R., and Titov, I · 2019
Later among the works it cites.
Separable manifold geometry in macaque ventral stream and dcnns
Chung, S., Dapello, J., Cohen, U., DiCarlo, J., and Sompolinsky, H · 2020
Closest in time.