Blog(3)

• Drop-DTW: A Differentiable Method for Sequence Alignment that can Handle Outliers

  The problem of sequence-to-sequence alignment is central to many computational applications. Aligning two sequences (e.g., temporal signals) entails computing the optimal pairwise correspondence between the sequence elements while preserving their match orderings.

• Is Your HD Map Constructor Reliable under Sensor Corruptions?

  HD maps are fundamental components in autonomous driving systems, providing centimeter-level details of traffic rules, vectorized topology, and navigation information.

• FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

  Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices. A popular alternative comprises offloading CNN processing to powerful cloud-based servers. Nevertheless, by relying on the cloud to produce outputs, emerging mission-critical and high-mobility applications, such as drone obstacle avoidance or interactive applications, can suffer from the dynamic connectivity conditions and the uncertain availability of the cloud. In this paper we propose SPINN:

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Publications(27)

• FedL2P: Federated Learning to Personalize 

  AuthorRoyson Lee, Minyoung Kim, Da Li, Timothy Hospedales

  PublishedConference on Neural Information Processing Systems (NeurIPS)

  Date2023-12-11

• BayesTune: Bayesian Sparse Deep Model Fine-tuning

  AuthorMinyoung Kim, Timothy Hospedales

  PublishedConference on Neural Information Processing Systems (NeurIPS)

  Date2023-12-11

• CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss

  AuthorRakshith Srinivasa,Jaejin Cho,Chouchang Yang,Yashas Malur Saidutta,Chinghua Lee,Yilin Shen,Hongxia Jin

  PublishedNeural Information Processing Systems (NeurIPS)

  Date2023-12-10

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Others(1)

• Samsung AI Center - Moscow

  About Us Papers Play Samsung AI Center – Moscow is the first institution in Russia focused directly on artificial intelligence (AI), where Russia’s strengths and capabilities in fundamental sciences, especially mathematics and physics, are brought together with Samsung global R&D strategy. Samsung AI Center - Moscow is led by Dr. Anton Konushin who is world-known AI Computer Vision expert and associate professor at Moscow State University and Higher School of Econo...