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Collaborating Authors

 Rudra, Archisman


Fragment Completion in Humans and Machines

Neural Information Processing Systems

Partial information can trigger a complete memory. At the same time, human memory is not perfect. A cue can contain enough information to specify an item in memory, but fail to trigger that item. In the context of word memory, we present experiments that demonstrate some basic patterns in human memory errors. We use cues that consist of word fragments. We show that short and long cues are completed more accurately than medium length ones and study some of the factors that lead to this behavior. We then present a novel computational model that shows some of the flexibility and patterns of errors that occur in human memory.


Fragment Completion in Humans and Machines

Neural Information Processing Systems

Partial information can trigger a complete memory. At the same time, human memory is not perfect. A cue can contain enough information to specify an item in memory, but fail to trigger that item. In the context of word memory, we present experiments that demonstrate some basic patterns in human memory errors. We use cues that consist of word fragments. Weshow that short and long cues are completed more accurately than medium length ones and study some of the factors that lead to this behavior. We then present a novel computational model that shows some of the flexibility and patterns of errors that occur in human memory.


Features as Sufficient Statistics

Neural Information Processing Systems

An image is often represented by a set of detected features. We get an enormous compression by representing images in this way. Furthermore, we get a representation which is little affected by small amounts of noise in the image. However, features are typically chosen in an ad hoc manner.


Features as Sufficient Statistics

Neural Information Processing Systems

An image is often represented by a set of detected features. We get an enormous compression by representing images in this way. Furthermore, weget a representation which is little affected by small amounts of noise in the image. However, features are typically chosen in an ad hoc manner.


Features as Sufficient Statistics

Neural Information Processing Systems

An image is often represented by a set of detected features. We get an enormous compression by representing images in this way. Furthermore, we get a representation which is little affected by small amounts of noise in the image. However, features are typically chosen in an ad hoc manner.