Publications: Recent submissions

Now showing items 121-123 of 160

    • I-theory on depth vs width: hierarchical function composition 

      Poggio, Tomaso; Anselmi, Fabio; Rosasco, Lorenzo (Center for Brains, Minds and Machines (CBMM), 2015-12-29)
      Deep learning networks with convolution, pooling and subsampling are a special case of hierar- chical architectures, which can be represented by trees (such as binary trees). Hierarchical as well as shallow networks can ...

    • UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS 

      Lotter, William; Kreiman, Gabriel; Cox, David (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-12-15)
      The ability to predict future states of the environment is a central pillar of intelligence. At its core, effective prediction requires an internal model of the world and an understanding of the rules by which the world ...

    • Holographic Embeddings of Knowledge Graphs 

      Nickel, Maximilian; Rosasco, Lorenzo; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-11-16)
      Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn ...