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On small depth threshold circuits
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf01137685openalex-snapshot
Lower bounds on the size of bounded depth circuits over a complete basis with logical addition
inferredcites →https://doi.org/10.1007/bf01200426openalex-snapshot
Majority gates vs. general weighted threshold gates
inferredcites →https://doi.org/10.1016/0020-0190(91)90183-iopenalex-snapshot
Some notes on threshold circuits, and multiplication in depth 4
inferredcites →https://doi.org/10.1016/0893-6080(94)90014-0openalex-snapshot
Introduction to the theory of neural computation
inferredcites →https://doi.org/10.1063/1.2810360openalex-snapshot
Introduction to the Theory of Neural Computation
inferredcites →https://doi.org/10.1109/12.45216openalex-snapshot
A spectral lower bound technique for the size of decision trees and two-level AND/OR circuits
inferredcites →https://doi.org/10.1109/18.256501openalex-snapshot
Depth efficient neural networks for division and related problems
inferredcites →https://doi.org/10.1109/acssc.1999.831923openalex-snapshot
Optimal-depth threshold circuits for multiplication and related problems
inferredcites →https://doi.org/10.1109/fscs.1990.89582openalex-snapshot
On the power of small-depth threshold circuits
inferredcites →https://doi.org/10.1109/fscs.1990.89583openalex-snapshot
ON ACC and threshold circuits
inferredcites →https://doi.org/10.1109/sct.1991.160275openalex-snapshot
Geometric arguments yield better bounds for threshold circuits and distributed computing
inferredcites →https://doi.org/10.1109/sct.1992.215375openalex-snapshot
Majority gates vs. general weighted threshold gates
inferredcites →https://doi.org/10.1109/sfcs.1987.59openalex-snapshot
Threshold circuits of bounded depth
inferredcites →https://doi.org/10.1109/sfcs.1988.21923openalex-snapshot
The influence of variables on Boolean functions
inferredcites →https://doi.org/10.1109/sfcs.1989.63537openalex-snapshot
Constant depth circuits, Fourier transform, and learnability
inferredcites →https://doi.org/10.1109/sfcs.1991.185447openalex-snapshot
On the computational power of sigmoid versus Boolean threshold circuits
inferredcites →https://doi.org/10.1109/sfcs.1991.185448openalex-snapshot
Variation ranks of communication matrices and lower bounds for depth two circuits having symmetric gates with unbounded fan-in
inferredcites →https://doi.org/10.1109/tec.1961.5219204openalex-snapshot
On the Size of Weights Required for Linear-Input Switching Functions
inferredcites →https://doi.org/10.1137/0215070openalex-snapshot
Log Depth Circuits for Division and Related Problems
inferredcites →https://doi.org/10.1137/0221003openalex-snapshot
Polynomial Threshold Functions, $AC^0 $ Functions, and Spectral Norms
inferredcites →https://doi.org/10.1137/0403015openalex-snapshot
Harmonic Analysis of Polynomial Threshold Functions
inferredcites →https://doi.org/10.1137/0404038openalex-snapshot
On the Power of Threshold Circuits with Small Weights
inferredcites →https://doi.org/10.1137/s0895480191218496openalex-snapshot
Explicit Constructions of Depth-2 Majority Circuits for Comparison and Addition
inferredcites →https://doi.org/10.1145/103418.103466openalex-snapshot
Learning decision trees using the Fourier spectrum
inferredcites →https://doi.org/10.1145/28395.28404openalex-snapshot
Algebraic methods in the theory of lower bounds for Boolean circuit complexity
inferredcites →https://doi.org/10.1145/800135.804414openalex-snapshot
Some complexity questions related to distributive computing(Preliminary Report)
inferredcites →https://doi.org/10.1147/rd.312.0235openalex-snapshot
The complexity of computations by networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.3833584openalex-extracted
A Study of the Mathematics of Deep Learning
inferred← citeshttps://doi.org/10.1007/11549345_11openalex-snapshot
On the Complexity of Depth-2 Circuits with Threshold Gates
inferred← citeshttps://doi.org/10.1007/3-540-60246-1_135openalex-snapshot
Multiple product modulo arbitrary numbers
inferred← citeshttps://doi.org/10.1007/3-540-61440-0_116openalex-snapshot
Lower bounds for prepositional proofs and independence results in bounded arithmetic
inferred← citeshttps://doi.org/10.1007/3-540-62034-6_33openalex-snapshot
Circuit complexity before the dawn of the new millennium
inferred← citeshttps://doi.org/10.1007/978-1-4615-2696-4_2openalex-snapshot
Depth-Efficient Threshold Circuits for Arithmetic Functions
inferred← citeshttps://doi.org/10.1007/978-1-4615-2696-4_3openalex-snapshot
Communication Complexity and Lower Bounds for Threshold Circuits
inferred← citeshttps://doi.org/10.1007/978-3-030-11479-4_16openalex-snapshot
Reducing Hierarchical Deep Learning Networks as Game Playing Artefact Using Regret Matching
inferred← citeshttps://doi.org/10.1007/978-3-030-64694-3_9openalex-snapshot
DeepClone: Modeling Clones to Generate Code Predictions
inferred← citeshttps://doi.org/10.1007/978-3-642-30870-3_60openalex-snapshot
Lower Bound on Weights of Large Degree Threshold Functions
inferred← citeshttps://doi.org/10.1007/978-3-642-60539-0_34openalex-snapshot
Linear decision lists and partitioning algorithms for the construction of neural networks
inferred← citeshttps://doi.org/10.1007/s00037-007-0228-7openalex-snapshot
Every Linear Threshold Function has a Low-Weight Approximator
inferred← citeshttps://doi.org/10.1007/s10994-011-5262-7openalex-snapshot
Linear classifiers are nearly optimal when hidden variables have diverse effects
inferred← citeshttps://doi.org/10.1016/0020-0190(93)90041-7openalex-snapshot
nω(log n) lower bounds on the size of depth-3 threshold cicuits with AND gates at the bottom
inferred← citeshttps://doi.org/10.1016/0020-0190(93)90202-kopenalex-snapshot
Optimal lower bounds on the depth of polynomial-size threshold circuits for some arithmetic functions
inferred← citeshttps://doi.org/10.1016/0020-0190(95)00137-2openalex-snapshot
Computing threshold functions by depth-3 threshold circuits with smaller thresholds of their gates
inferred← citeshttps://doi.org/10.1016/0893-6080(96)00130-xopenalex-snapshot
On the Circuit Complexity of Sigmoid Feedforward Neural Networks
inferred← citeshttps://doi.org/10.1016/j.ic.2013.04.002openalex-snapshot
Decomposition of threshold functions into bounded fan-in threshold functions
inferred← citeshttps://doi.org/10.1016/j.ipl.2006.11.003openalex-snapshot
Powering requires threshold depth 3
inferred← citeshttps://doi.org/10.1016/j.tcs.2018.04.038openalex-snapshot
Circuit lower bounds from learning-theoretic approaches
inferred← citeshttps://doi.org/10.1016/s0893-6080(02)00032-1openalex-snapshot
On the capabilities of neural networks using limited precision weights
inferred← citeshttps://doi.org/10.1070/rm2003v058n05abeh000667openalex-snapshot
Monotone Boolean functions
inferred← citeshttps://doi.org/10.1109/ccc.2006.18openalex-snapshot
Every Linear Threshold Function has a Low-Weight Approximator
inferred← citeshttps://doi.org/10.1109/focs.2017.81openalex-snapshot
Fooling Intersections of Low-Weight Halfspaces
inferred← citeshttps://doi.org/10.1109/focs.2018.00014openalex-snapshot
A Short List of Equalities Induces Large Sign Rank
inferred← citeshttps://doi.org/10.1109/sfcs.1998.743456openalex-snapshot
Exponential complexity lower bounds for depth 3 arithmetic circuits in algebras of functions over finite fields
inferred← citeshttps://doi.org/10.1134/s0081543811060149openalex-snapshot
Degree-uniform lower bound on the weights of polynomials with given sign function
inferred← citeshttps://doi.org/10.1137/120868402openalex-snapshot
On the Weight of Halfspaces over Hamming Balls
inferred← citeshttps://doi.org/10.1137/19m1271348openalex-snapshot
A Short List of Equalities Induces Large Sign-Rank
inferred← citeshttps://doi.org/10.1137/s0097539794274519openalex-snapshot
Simulating Threshold Circuits by Majority Circuits
inferred← citeshttps://doi.org/10.1145/167088.167234openalex-snapshot
Simulating threshold circuits by majority circuits
inferred← citeshttps://doi.org/10.1145/2422436.2422440openalex-snapshot
Low-weight halfspaces for sparse boolean vectors
inferred← citeshttps://doi.org/10.1145/2591796.2591858openalex-snapshot
New algorithms and lower bounds for circuits with linear threshold gates
inferred← citeshttps://doi.org/10.1162/089976603322518731openalex-snapshot
General-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results
inferred← citeshttps://doi.org/10.2168/lmcs-9(2:13)2013openalex-snapshot
Lower Bound on Weights of Large Degree Threshold Functions
inferred← citeshttps://doi.org/10.4230/lipics.approx-random.2019.56openalex-snapshot
String Matching: Communication, Circuits, and Learning
inferred← citeshttps://doi.org/10.4230/lipics.ccc.2018.6openalex-snapshot
Limits on Representing Boolean Functions by Linear Combinations of Simple Functions: Thresholds, ReLUs, and Low-Degree Polynomials
inferred← citeshttps://doi.org/10.48550/arxiv.1401.2444openalex-snapshot
New algorithms and lower bounds for circuits with linear threshold gates
inferred← citeshttps://doi.org/10.48550/arxiv.1409.3215openalex-snapshot
Sequence to Sequence Learning with Neural Networks
inferred← citeshttps://doi.org/10.48550/arxiv.1705.02397openalex-snapshot
Weights at the Bottom Matter When the Top is Heavy
inferred← citeshttps://doi.org/10.48550/arxiv.2006.00625openalex-snapshot
Neural Networks with Small Weights and Depth-Separation Barriers
inferred← citeshttps://doi.org/10.48550/arxiv.2102.00314openalex-snapshot
Size and Depth Separation in Approximating Benign Functions with Neural Networks
inferred← citeshttps://doi.org/10.7282/t35d8qvqopenalex-snapshot
On circuit complexity classes and iterated matrix multiplication
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