TA-L.A.1

THE RISE OF THE LOTTERY HEROES: WHY ZERO-SHOT PRUNING IS HARD

Enzo Tartaglione, LTCI, Telecom Paris, Institut Polytechnique de Paris, France
Session:
SCENA: Simplification, Compression and Efficiency with Neural networks and Artificial intelligence
Lecture
Special Session
Track:
Special Sessions
Location:
Room A
Presentation Time:
Tue, 18 Oct, 16:30 - 16:45 China Standard Time (UTC +8)
Tue, 18 Oct, 10:30 - 10:45 Central European Time (UTC +2)
Tue, 18 Oct, 08:30 - 08:45 UTC
Tue, 18 Oct, 04:30 - 04:45 Eastern Time (UTC -4)
Session Co-Chairs:
Attilio Fiandrotti, Università di Torino and Enzo Tartaglione, Télécom Paris, IP Paris
Presentation
Discussion
Resources
No resources available.
Session TA-L.A
TA-L.A.1: THE RISE OF THE LOTTERY HEROES: WHY ZERO-SHOT PRUNING IS HARD
Enzo Tartaglione, LTCI, Telecom Paris, Institut Polytechnique de Paris, France
TA-L.A.2: Efficient Inference of Image-based Neural Network Models in Reconfigurable Systems with Pruning and Quantization
Jose Flich, Laura Medina, Izan Catalán, Carles Hernández, Universitat Politecnica de Valencia, Spain; Andrea Bragagnolo, Universita degli Studi di Torino, Italy; Fabrice Auzanneau, David Briand, Université Paris Saclay, France
TA-L.A.3: TOWARDS EFFICIENT CAPSULE NETWORKS
Riccardo Renzulli, Marco Grangetto, University of Turin, Italy
TA-L.A.4: Stochastic Binary-Ternary Quantization for Communication Efficient Federated Computation
Goutham Rangu, Homayun Afrabandpey, Francesco Cricri, Honglei Zhang, Emre Aksu, Miska Hannuksela, Hamed R. Tavakoli, Nokia Technologies, Finland
TA-L.A.5: FORGETFUL ACTIVE LEARNING WITH SWITCH EVENTS: EFFICIENT SAMPLING FOR OUT-OF-DISTRIBUTION DATA
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib, Georgia Institute of Technology, United States of America
TA-L.A.6: SIMULTANEOUS LEARNING AND COMPRESSION FOR CONVOLUTION NEURAL NETWORKS
Muhammad Tayyab, Abhijit Mahalanobis, University Of Central Florida, United States of America