深度学习读书会

来自集智百科
跳转到: 导航搜索

因为文献太多一次读书会不可能面面俱到,采取follow每个领域最重要的1~2个研究者的最具代表性的工作的方式,挑选出下面文章重点研读。文章前的标签代表类型,NB=神经生物学发现,CM=计算模型,ML=机器学习算法。

目录

脑与deep learning读书会主讲人联系方式

姓名 专业背景 电邮
张巍
于翮
刘彦平
王长明
王静
肖达 北京邮电大学计算机学院 教师
曹旭东
袁行远
吴令飞
何永振

Overview: deep architecture in brain and machine(1次)

主讲人:肖达

时间:2013年6月16日

相关领域研究者:James DiCarlo

  • 【NB】Chris I. Baker (2004) Visual Processing in the Primate Brain. In "Handbook of Psychology, Biological Psychology", Wiley.
  • 【NB】【CM】DiCarlo JJ, Zoccolan D, Rust NC. (2012) How does the brain solve visual object recognition? Neuron, 73(3):415-34.
  • 【CM】Cadieu CF, et al. (2013) The Neural Representation Benchmark and its Evaluation on Brain and Machine. International Conference on Learning Representations (ICLR) 2013.

Early visual system (retinal ganglion cell, LGN, V1), canonical cortical circuits(1.5次)

集智俱乐部《脑与deep learning读书会》第2期

题目

Descriptive, Mechanistic and Interpretive Models of Primary Visual Cortex

主讲人

  • 肖达,北京邮电大学计算机学院教师。
  • 袁行远,前淘宝网数据挖掘与并行计算高级算法工程师,现辞职休假中。

提纲

  1. Descriptive models (What):
    • Responses of a Neuron in an Intact Cat Brain, (Hubel Wiesel http://www.youtube.com/watch?v=8VdFf3egwfg 墙内视频http://v.youku.com/v_show/id_XNDc0MTkxODc2.html)
    • Contrast sensitivity of Human
    • Receptive Fields and Edges Detection Program Demo
  2. Machanistic Models (How):
    • Oriented Receptive Fields and Position-Less Receptive Fields
    • Fourier Decomposition hypothesis
    • Build Self-Organizing Map for V1
  3. Interpretive Models (Why):
    • What is the Best Multi-Stage Architecture for Object Recognition
  4. The columnar organization of the neocortex and its implication for computer vision

参考文献

  • 【NB】Matteo Carandini (2012) Area V1. Scholarpedia, 7(7):12105. http://www.scholarpedia.org/article/Area_V1
  • 【NB】【CM】Carandini M, et al. (2005) Do we know what the early visual system does? Journal of Neuroscience, 25:10577-10597.
  • 【NB】Douglas, RJ and Martin, KAC (2007) Recurrent neuronal circuits in the neocortex. Current Opinion in Biology, 17:496-500.
  • 【NB】Douglas, RJ and Martin, KAC (2010) Canonical cortical circuits. Chapter 2 in Handbook of Brain Microcircuits 15-21.
  • 【ML】Kevin Jarrett, Koray Kavukcuoglu, Marc’Aurelio Ranzato, and Yann LeCun. (2009) What is the Best Multi-Stage Architecture for Object Recognition? in Proc. International Conference on Computer Vision (ICCV’09).

learning features(selectivity) & sparse coding, cortical maps(0.5次)

Bruno Olshausen

【CM】Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision Research, 37 (23), 3311-3325.

【CM】Bednar JA. (2012) Building a mechanistic model of the development and function of the primary visual cortex. Journal of Physiology (Paris), 106:194-211.

learning transformations(invariance)(1次)

Aapo Hyvarinen, Yan Karklin

【CM】Hyvarinen, A. and Hoyer, P. (2001). A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images. Vision Research, 41(18):2413–2423.

【CM】Karklin, Y., & Lewicki, M. S. (2009). Emergence of complex cell properties by learning to generalize in natural scenes. Nature, 457(7225), 83-85.

【CM】Adelson E.H. and Bergen J.R. (1985) Spatiotemporal energy models for the perception of motion. Journal Opt. Soc. Am.

【ML】Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, and Andrew Y. Ng. (2009) Measuring invariances in deep networks. Advances in Neural Information Processing Systems (NIPS).

补充

【ML】Q.V. Le, et, al. Building high-level features using large scale unsupervised learning. ICML, 2012.


V2(1次)

【NB】Lawrence C. Sincich and Jonathan C. Horton (2005) The Circuitry of V1 and V2: Integration of Color, Form, and Motion. Annu. Rev. Neurosci. 28:303–26.

【NB】Roe AW, Lu HD, Chen G (2008) Functional architecture of area V2. Encyclopedia of Neuroscience (Squire L, ed.). Elsevier, Oxford, UK.

【CM】Cadieu C.F. & Olshausen B.A. (2012) Learning Intermediate-Level Representations of Form and Motion from Natural Movies. Neural Computation.

【ML】Zou, W.Y., Zhu, S., Ng, A., and Yu, K. (2012) Deep learning of invariant features via simulated fixations in video. In Advances in Neural Information Processing Systems (NIPS).

【CM】Gutmann MU & Hyvarinen A (2013) A three-layer model of natural image statistics. Journal of Physiology-Paris.

专题讨论:learning mid-level features(形式待定)

【ML】Memisevic, R., Exarchakis, G. (2013) Learning invariant features by harnessing the aperture problem. International Conference on Machine Learning (ICML).

【ML】Kihyuk Sohn, Guanyu Zhou, Chansoo Lee, and Honglak Lee. (2013) Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines。 Proceedings of the 30th International Conference on Machine Learning (ICML).

【ML】Roni Mittelman, Honglak Lee, Benjamin Kuipers, and Silvio Savarese. (2013) Weakly Supervised Learning of Mid-Level Features with Beta-Bernoulli Process Restricted Boltzmann Machines. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

V4, shape perception(1次)

Charles E. Connor

【NB】Pasupathy, A. & Connor, C.E. (2002) Population coding of shape in area V4. Nature Neuroscience 5: 1332-1338.

【NB】Connor, C.E. (2007) Transformation of shape information in the ventral pathway. Current Opinion in Neurobiology 17: 140-147.

【NB】【CM】Roe AW, et al. (2012) Towards a unified theory of visual area V4. Neuron 74(2):12-29.

【NB】【CM】Cadieu C, Kouh M, Pasupathy A, Connor CE, Riesenhuber M, Poggio T. (2007) A model of V4 shape selectivity and invariance. Journal of Neurophysiology, 98(3), 1733-50.

IT, object & face recognition(1次)

Keiji Tanaka, Doris Tsao

【NB】Charles G. Gross (2008) Inferior temporal cortex. Scholarpedia, 3(12):7294. http://www.scholarpedia.org/article/Inferior_temporal_cortex

【NB】Tanaka, K. (1996). Inferotemporal cortex and object vision. Annual Review of Neuroscience, 19, 109–139.

【NB】【CM】Tsao DY, Livingstone, MS. (2008) Mechanisms for face perception. Annual Review of Neuroscience, 31: 411-438.

【NB】【CM】Tsao D.Y., Cadieu C. and Livingstone M. (2010) Object Recognition: Physiological and Computational Insights. Chapter 24 in Primate Neuroethology. Edited by M. Platt and A. Ghazanfar. Oxford University Press.

海马体,记忆,睡眠(1次)

待补充

视觉系统的发育和进化,低等动物的视觉(1次)

Jon Kaas

The Evolution Of The Visual System In Primates 待补充

应用实践

深度学习项目资源表

Deep Learning for Industrial Application 2.0

Deep Learning for Industrial Application

Deep Learning and it's Industrial Applications

深度学习在工业领域的应用汇总

Deep Learning Tutorial & Project Index

Pytorch & Project Index

个人工具
名字空间
操作
导航
工具箱