MLSP-L17.5
LEARNING REPRESENTATIONS FROM EXPLAINABLE AND CONNECTIONIST APPROACHES FOR VISUAL QUESTION ANSWERING
Aakansha Mishra, Srinivas S Miriyala, Vikram N Rajendiran, Samsung Research Institute Bangalore (SRIB), India
Session:
MLSP-L17: Explainable and Interpretable Machine Learning I Lecture
Track:
Machine Learning for Signal Processing
Location:
Room E2
Presentation Time:
Thu, 18 Apr, 14:30 - 14:50 (UTC +9)
Session Co-Chairs:
Constantine Kotropoulos, Aristotle University of Thessaloniki and Mohsen Naqvi, Newcastle University
Session MLSP-L17
MLSP-L17.1: RENYI DIVERGENCES LEARNING FOR EXPLAINABLE CLASSIFICATION OF SAR IMAGE PAIRS
Matthieu Gallet, Ammar Mian, Abdourrahmane Atto, University Savoie Mont Blanc, France
MLSP-L17.2: INTERPRETABLE FACE AGING: ENHANCING CONDITIONAL ADVERSARIAL AUTOENCODERS WITH LIME EXPLANATIONS
Christos Korgialas, Evangelia Pantraki, Constantine Kotropoulos, Aristotle University of Thessaloniki, Greece
MLSP-L17.3: SPASE: SPAtial Saliency Explanation for time series models
Pranay Lohia, Badri Narayana Patro, Naveen Panwar, Vijay Agneeswaran, Microsoft, India
MLSP-L17.4: ACTIVE EXPLAINABLE RECOMMENDATION WITH LIMITED LABELING BUDGETS
Jingsen Zhang, Renmin University of China, China; Xiaohe Bo, Beijing Normal University, China; Chenxi Wang, University of Electronic Science and Technology of China, China; Quanyu Dai, Zhenhua Dong, Ruiming Tang, Huawei Noah’s Ark Lab, China; Xu Chen, Renmin University of China, China
MLSP-L17.5: LEARNING REPRESENTATIONS FROM EXPLAINABLE AND CONNECTIONIST APPROACHES FOR VISUAL QUESTION ANSWERING
Aakansha Mishra, Srinivas S Miriyala, Vikram N Rajendiran, Samsung Research Institute Bangalore (SRIB), India
Contacts