Documents dont l'auteur est "Bilaniuk, Olexa"

A

Askarihemmat, M., Wagner, S., Bilaniuk, O., Hariri, Y., Savaria, Y., & David, J. P. (janvier 2023). BARVINN: Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU [Communication écrite]. 28th Asia and South Pacific Design Automation Conference (ASP-DAC 2023), Tokyo, Japan. Lien externe

AskariHemmat, M.H., Bilaniuk, O., Wagner, S., Savaria, Y., & David, J. P. (mai 2021). RISC-V barrel processor for deep neural network acceleration [Communication écrite]. 53rd IEEE International Symposium on Circuits and Systems (ISCAS 2021), Daegu, Korea (5 pages). Lien externe

Askarihemmat, M.H., Bilaniuk, O., Wagner, S., Savaria, Y., & David, J. P. (mai 2020). RISC-V Barrel Processor for Accelerator Control [Communication écrite]. 28th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM 2020), Fayetteville, AR (1 page). Lien externe

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Bengio, Y., Deleu, T., Rahaman, N., Ke, N. R., Lachapelle, S., Bilaniuk, O., Goyal, A., & Pal, C. J. (avril 2020). A meta-transfer objective for learning to disentagle causal mechanisms [Communication écrite]. 8th International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia (27 pages). Lien externe

Bilaniuk, O., Wagner, S., Savaria, Y., & David, J. P. (mai 2019). Bit-slicing FPGA accelerator for quantized neural networks [Communication écrite]. IEEE International Symposium on Circuits and Systems (ISCAS 2019), Sapporo, Japan (5 pages). Lien externe

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Ke, N. R., Goyal, A., Bilaniuk, O., Binas, J., Mozer, M. C., Pal, C. J., & Bengio, Y. (décembre 2018). Sparse attentive backtracking: Temporal credit assignment through reminding [Communication écrite]. 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada (12 pages). Lien externe

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Trabelsi, C., Bilaniuk, O., Zhang, Y., Serdyuk, D., Subramanian, S., Santos, J. F., Mehri, S., Rostamzadeh, N., Bengio, Y., & Pal, C. J. (avril 2018). Deep complex networks [Communication écrite]. 6th International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada (19 pages). Lien externe