4,857 research outputs found
On palimpsests in neural memory: an information theory viewpoint
The finite capacity of neural memory and the
reconsolidation phenomenon suggest it is important to be able
to update stored information as in a palimpsest, where new
information overwrites old information. Moreover, changing
information in memory is metabolically costly. In this paper, we
suggest that information-theoretic approaches may inform the
fundamental limits in constructing such a memory system. In
particular, we define malleable coding, that considers not only
representation length but also ease of representation update,
thereby encouraging some form of recycling to convert an old
codeword into a new one. Malleability cost is the difficulty of
synchronizing compressed versions, and malleable codes are of
particular interest when representing information and modifying
the representation are both expensive. We examine the tradeoff
between compression efficiency and malleability cost, under a
malleability metric defined with respect to a string edit distance.
This introduces a metric topology to the compressed domain. We
characterize the exact set of achievable rates and malleability as
the solution of a subgraph isomorphism problem. This is all done
within the optimization approach to biology framework.Accepted manuscrip
Scalable Interactive Volume Rendering Using Off-the-shelf Components
This paper describes an application of a second generation implementation of the Sepia architecture (Sepia-2) to interactive volu-metric visualization of large rectilinear scalar fields. By employingpipelined associative blending operators in a sort-last configuration a demonstration system with 8 rendering computers sustains 24 to 28 frames per second while interactively rendering large data volumes (1024x256x256 voxels, and 512x512x512 voxels). We believe interactive performance at these frame rates and data sizes is unprecedented. We also believe these results can be extended to other types of structured and unstructured grids and a variety of GL rendering techniques including surface rendering and shadow map-ping. We show how to extend our single-stage crossbar demonstration system to multi-stage networks in order to support much larger data sizes and higher image resolutions. This requires solving a dynamic mapping problem for a class of blending operators that includes Porter-Duff compositing operators
Parallel Metric Tree Embedding based on an Algebraic View on Moore-Bellman-Ford
A \emph{metric tree embedding} of expected \emph{stretch~}
maps a weighted -node graph to a weighted tree with such that, for all ,
and
. Such embeddings are highly useful for designing
fast approximation algorithms, as many hard problems are easy to solve on tree
instances. However, to date the best parallel -depth algorithm that achieves an asymptotically optimal expected stretch of
requires
work and a metric as input.
In this paper, we show how to achieve the same guarantees using
depth and
work, where and is an arbitrarily small constant.
Moreover, one may further reduce the work to at the expense of increasing the expected stretch to
.
Our main tool in deriving these parallel algorithms is an algebraic
characterization of a generalization of the classic Moore-Bellman-Ford
algorithm. We consider this framework, which subsumes a variety of previous
"Moore-Bellman-Ford-like" algorithms, to be of independent interest and discuss
it in depth. In our tree embedding algorithm, we leverage it for providing
efficient query access to an approximate metric that allows sampling the tree
using depth and work.
We illustrate the generality and versatility of our techniques by various
examples and a number of additional results
Energy Transformer
Transformers have become the de facto models of choice in machine learning,
typically leading to impressive performance on many applications. At the same
time, the architectural development in the transformer world is mostly driven
by empirical findings, and the theoretical understanding of their architectural
building blocks is rather limited. In contrast, Dense Associative Memory models
or Modern Hopfield Networks have a well-established theoretical foundation, but
have not yet demonstrated truly impressive practical results. We propose a
transformer architecture that replaces the sequence of feedforward transformer
blocks with a single large Associative Memory model. Our novel architecture,
called Energy Transformer (or ET for short), has many of the familiar
architectural primitives that are often used in the current generation of
transformers. However, it is not identical to the existing architectures. The
sequence of transformer layers in ET is purposely designed to minimize a
specifically engineered energy function, which is responsible for representing
the relationships between the tokens. As a consequence of this computational
principle, the attention in ET is different from the conventional attention
mechanism. In this work, we introduce the theoretical foundations of ET,
explore it's empirical capabilities using the image completion task, and obtain
strong quantitative results on the graph anomaly detection task
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
We present, GeniePath, a scalable approach for learning adaptive receptive
fields of neural networks defined on permutation invariant graph data. In
GeniePath, we propose an adaptive path layer consists of two complementary
functions designed for breadth and depth exploration respectively, where the
former learns the importance of different sized neighborhoods, while the latter
extracts and filters signals aggregated from neighbors of different hops away.
Our method works in both transductive and inductive settings, and extensive
experiments compared with competitive methods show that our approaches yield
state-of-the-art results on large graphs
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