Browsing CBMM Memo Series by Title
Now showing items 47-66 of 141
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Fast, invariant representation for human action in the visual system
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-01-06)The ability to recognize the actions of others from visual input is essential to humans' daily lives. The neural computations underlying action recognition, however, are still poorly understood. We use magnetoencephalography ... -
Feature learning in deep classifiers through Intermediate Neural Collapse
(Center for Brains, Minds and Machines (CBMM), 2023-02-27)In this paper, we conduct an empirical study of the feature learning process in deep classifiers. Recent research has identified a training phenomenon called Neural Collapse (NC), in which the top-layer feature embeddings ... -
For interpolating kernel machines, the minimum norm ERM solution is the most stable
(Center for Brains, Minds and Machines (CBMM), 2020-06-22)We study the average CVloo stability of kernel ridge-less regression and derive corresponding risk bounds. We show that the interpolating solution with minimum norm has the best CVloo stability, which in turn is controlled ... -
Foveation-based Mechanisms Alleviate Adversarial Examples
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-01-19)We show that adversarial examples, i.e., the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in ... -
From Associative Memories to Deep Networks
(Center for Brains, Minds and Machines (CBMM), 2021-01-12)About fifty years ago, holography was proposed as a model of associative memory. Associative memories with similar properties were soon after implemented as simple networks of threshold neurons by Willshaw and Longuet-Higgins. ... -
From Marr’s Vision to the Problem of Human Intelligence
(Center for Brains, Minds and Machines (CBMM), 2021-09-01) -
Full interpretation of minimal images
(Center for Brains, Minds and Machines (CBMM), 2017-02-08)The goal in this work is to model the process of ‘full interpretation’ of object images, which is the ability to identify and localize all semantic features and parts that are recognized by human observers. The task is ... -
The Genesis Story Understanding and Story Telling System A 21st Century Step toward Artificial Intelligence
(Center for Brains, Minds and Machines (CBMM), 2014-06-10)Story understanding is an important differentiator of human intelligence, perhaps the most important differentiator. The Genesis system was built to model and explore aspects of story understanding using simply expressed, ... -
Group Invariant Deep Representations for Image Instance Retrieval
(Center for Brains, Minds and Machines (CBMM), 2016-01-11)Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in large scale image classification, ... -
Hierarchically Local Tasks and Deep Convolutional Networks
(Center for Brains, Minds and Machines (CBMM), 2020-06-24)The main success stories of deep learning, starting with ImageNet, depend on convolutional networks, which on certain tasks perform significantly better than traditional shallow classifiers, such as support vector machines. ... -
Hippocampal Remapping as Hidden State Inference
(Center for Brains, Minds and Machines (CBMM), bioRxiv, 2019-08-22)Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact ... -
Holographic Embeddings of Knowledge Graphs
(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 ... -
A Homogeneous Transformer Architecture
(Center for Brains, Minds and Machines (CBMM), 2023-09-18)While the Transformer architecture has made a substantial impact in the field of machine learning, it is unclear what purpose each component serves in the overall architecture. Heterogeneous nonlinear circuits such as ... -
How Important is Weight Symmetry in Backpropagation?
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-11-29)Gradient backpropagation (BP) requires symmetric feedforward and feedback connections—the same weights must be used for forward and backward passes. This “weight transport problem” [1] is thought to be one of the main ... -
Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-15)Recent trends in image understanding have pushed for holistic scene understanding models that jointly reason about various tasks such as object detection, scene recognition, shape analysis, contextual reasoning, and local ... -
I-theory on depth vs width: hierarchical function composition
(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 ... -
Image interpretation above and below the object level
(Center for Brains, Minds and Machines (CBMM), 2018-05-10)Computational models of vision have advanced in recent years at a rapid rate, rivaling in some areas human- level performance. Much of the progress to date has focused on analyzing the visual scene at the object level – ... -
Image interpretation by iterative bottom-up top-down processing
(Center for Brains, Minds and Machines (CBMM), 2021-11-01)Scene understanding requires the extraction and representation of scene components, such as objects and their parts, people, and places, together with their individual properties, as well as relations and interactions ... -
Implicit dynamic regularization in deep networks
(Center for Brains, Minds and Machines (CBMM), 2020-08-17)Square loss has been observed to perform well in classification tasks, at least as well as crossentropy. However, a theoretical justification is lacking. Here we develop a theoretical analysis for the square loss that also ... -
Incorporating Rich Social Interactions Into MDPs
(Center for Brains, Minds and Machines (CBMM), International Conference on Robotics and Automation (ICRA), 2022-02-07)Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions originating from microso- ciology and economics ...
