Datasets:
Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
0 | 1 | 767
SELF-ORGANIZATION OF ASSOCIATIVE DATABASE
AND ITS APPLICATIONS
Hisashi Suzuki and Suguru Arimoto
Osaka University, Toyonaka, Osaka 560, Japan
ABSTRACT
An efficient method of self-organizing associative databases is proposed together with
applications to robot eyesight systems. The proposed databases can associate ... | 1 |@word trial:3 version:1 compression:3 instruction:1 km:1 delicately:1 recursively:1 initial:3 configuration:1 denoting:1 document:3 past:3 current:1 si:8 universality:1 written:3 must:2 realize:1 subsequent:1 periodically:1 distant:1 succeeding:1 sampl:1 stationary:1 half:2 selected:1 leaf:1 accordingly:6 xk:1 recor... |
1 | 10 | 683
A MEAN FIELD THEORY OF LAYER IV OF VISUAL CORTEX
AND ITS APPLICATION TO ARTIFICIAL NEURAL NETWORKS*
Christopher L. Scofield
Center for Neural Science and Physics Department
Brown University
Providence, Rhode Island 02912
and
Nestor, Inc., 1 Richmond Square, Providence, Rhode Island,
02906.
ABSTRACT
A single cell t... | 10 |@word proportion:1 open:2 independant:1 dramatic:1 reduction:1 initial:1 contains:1 exclusively:1 tuned:1 rearing:1 current:2 cad:1 ixil:1 plasticity:3 nervous:1 postnatal:1 dembo:1 ith:1 dissertation:1 location:2 preference:3 lessening:1 qualitative:1 consists:1 pathway:1 introduce:1 manner:2 proliferation:1 embod... |
2 | 100 | 394
STORING COVARIANCE BY THE ASSOCIATIVE
LONG?TERM POTENTIATION AND DEPRESSION
OF SYNAPTIC STRENGTHS IN THE HIPPOCAMPUS
Patric K. Stanton? and Terrence J. Sejnowski t
Department of Biophysics
Johns Hopkins University
Baltimore, MD 21218
ABSTRACT
In modeling studies or memory based on neural networks, both the select... | 100 |@word determinant:1 longterm:1 unaltered:1 middle:2 hippocampus:22 seems:1 hyperpolarized:2 open:1 pulse:2 covariance:12 lowfrequency:2 reduction:4 series:1 past:1 coactive:2 current:9 activation:5 yet:1 john:1 physiol:2 subsequent:2 hyperpolarizing:2 plasticity:8 aps:2 alone:6 patric:1 math:1 tpresent:1 burst:12 ... |
3 | 1,000 | Bayesian Query Construction for Neural
Network Models
Gerhard Paass
Jorg Kindermann
German National Research Center for Computer Science (GMD)
D-53757 Sankt Augustin, Germany
[email protected]
[email protected]
Abstract
If data collection is costly, there is much to be gained by actively selecting particularly informative ... | 1000 |@word trial:5 wcb:3 version:1 simulation:1 concise:1 tr:2 reduction:1 selecting:2 current:16 ixj:1 must:1 numerical:2 informative:1 dydx:1 analytic:1 drop:1 intelligence:1 selected:5 yr:3 beginning:1 toronto:1 wir:2 five:1 prove:1 expected:4 considering:1 project:1 sankt:1 titterington:1 control:1 unit:5 grant:1 ... |
4 | 1,001 | Neural Network Ensembles, Cross
Validation, and Active Learning
Anders Krogh"
Nordita
Blegdamsvej 17
2100 Copenhagen, Denmark
Jesper Vedelsby
Electronics Institute, Building 349
Technical University of Denmark
2800 Lyngby, Denmark
Abstract
Learning of continuous valued functions using neural network ensembles (commi... | 1001 |@word seems:1 thereby:1 solid:6 electronics:1 initial:1 scatter:1 enables:1 drop:1 plot:3 half:1 selected:1 intelligence:1 af3:4 five:3 consists:1 little:1 increasing:2 becomes:1 provided:1 lowest:2 israel:1 kind:2 developed:2 finding:1 ti:1 exactly:3 unit:1 positive:1 fluctuation:1 might:1 chose:1 mateo:2 sugges... |
5 | 1,002 | U sing a neural net to instantiate a
deformable model
Christopher K. I. Williams; Michael D. Revowand Geoffrey E. Hinton
Department of Computer Science, University of Toronto
Toronto, Ontario, Canada M5S lA4
Abstract
Deformable models are an attractive approach to recognizing nonrigid objects which have considerable w... | 1002 |@word deformed:2 trial:1 determinant:1 covariance:1 jacob:2 carry:1 initial:2 current:1 nowlan:1 must:1 readily:1 tot:2 predetermined:1 shape:7 hypothesize:1 designed:2 instantiate:4 guess:2 discovering:1 short:1 postal:1 toronto:4 location:7 sigmoidal:1 five:1 zii:3 along:3 constructed:1 consists:1 fitting:6 all... |
6 | 1,003 | Plasticity-Mediated Competitive Learning
Terrence J. Sejnowski
[email protected]
Nicol N. Schraudolph
[email protected]
Computational Neurobiology Laboratory
The Salk Institute for Biological Studies
San Diego, CA 92186-5800
and
Computer Science & Engineering Department
University of California, San Diego
La Jolla, CA 920... | 1003 |@word version:1 seems:1 seek:2 covariance:2 decorrelate:1 initial:1 past:1 comparing:1 activation:4 scatter:1 must:1 written:1 numerical:1 informative:1 plasticity:27 plot:2 update:1 discrimination:1 provides:3 node:22 preference:1 mathematical:1 prove:1 autocorrelation:1 frequently:1 inappropriate:1 begin:1 medi... |
7 | 1,004 | ICEG Morphology Classification using an
Analogue VLSI Neural Network
Richard Coggins, Marwan Jabri, Barry Flower and Stephen Pickard
Systems Engineering and Design Automation Laboratory
Department of Electrical Engineering J03,
University of Sydney, 2006, Australia.
Email: [email protected]
Abstract
An analogue... | 1004 |@word briefly:1 simulation:2 tried:1 accommodate:1 initial:1 born:1 amp:1 current:11 yet:1 must:2 icds:2 designed:1 update:1 alone:2 implying:1 prohibitive:1 device:6 selected:1 inspection:1 provides:2 node:1 ron:1 firstly:1 tinker:4 five:3 differential:3 m7:2 supply:1 consists:1 resistive:2 isscc:1 introduce:1 a... |
8 | 1,005 | "Real-Time Control of a Tokamak Plasma\nUsing Neural Networks\n\nChris M Bishop\nNeural Computing Re(...TRUNCATED) | "1005 |@word cox:2 loading:1 pulse:2 simulation:2 attainable:2 pressure:3 pick:2 thereby:1 solid:2 s(...TRUNCATED) |
9 | 1,006 | "Real-Time Control of a Tokamak Plasma\nUsing Neural Networks\n\nChris M Bishop\nNeural Computing Re(...TRUNCATED) | "1006 |@word pulsestream:4 cox:2 chromium:2 loading:1 simulation:2 pulse:9 attainable:2 pressure:3 p(...TRUNCATED) |
End of preview. Expand in Data Studio
NIPS
Some measurable characteristics of the dataset:
- D — number of documents
- W — modality dictionary size (number of unique tokens)
- len D — average document length in modality tokens (number of tokens)
- len D uniq — average document length in unique modality tokens (number of unique tokens)
| D | @word W | @word len D | @word len D uniq | |
|---|---|---|---|---|
| value | 7241 | 1.18333e+07 | 1634.21 | 644.49 |
Information about document lengths in modality tokens:
| len_total@word | len_uniq@word | |
|---|---|---|
| mean | 1634.21 | 644.49 |
| std | 481.923 | 162.31 |
| min | 0 | 0 |
| 25% | 1249 | 524 |
| 50% | 1663 | 641 |
| 75% | 1978 | 755 |
| max | 6000 | 1513 |
There are several dataset versions used in other works.
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