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On the Power of Threshold Circuits with Small Weights
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-16486-3_105openalex-snapshot
Parallel computation with threshold functions
inferredcites →https://doi.org/10.1016/0001-8708(79)90034-3openalex-snapshot
The theory of error-correcting codes
inferredcites →https://doi.org/10.1016/b978-0-12-509850-2.50010-5openalex-snapshot
HARMONIC ANALYSIS OF SWITCHING FUNCTIONS
inferredcites →https://doi.org/10.1109/pgec.1965.264090openalex-snapshot
A Lower Bound of the Number of Threshold Functions
inferredcites →https://doi.org/10.1109/pgec.1966.264494openalex-snapshot
Bounds on the Number of Threshold Functions
inferredcites →https://doi.org/10.1109/pgec.1966.264574openalex-snapshot
Lower Bound of the Number of Threshold Functions
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/10.1137/0403015openalex-snapshot
Harmonic Analysis of Polynomial Threshold Functions
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/10.1006/inco.1994.1057openalex-snapshot
Depth Reduction for Circuits of Unbounded Fan-in
inferred← citeshttps://doi.org/10.1006/inco.1998.2732openalex-snapshot
Threshold Circuits of Small Majority-Depth
inferred← citeshttps://doi.org/10.1006/jcss.1997.1478openalex-snapshot
A Weak Version of the Blum, Shub, and Smale Model
inferred← citeshttps://doi.org/10.1007/11549345_11openalex-snapshot
On the Complexity of Depth-2 Circuits with Threshold Gates
inferred← citeshttps://doi.org/10.1007/3-540-44989-2_89openalex-snapshot
Constructive Threshold Logic Addition
inferred← citeshttps://doi.org/10.1007/3-540-55706-7_4openalex-snapshot
On small depth threshold circuits
inferred← citeshttps://doi.org/10.1007/3-540-55808-x_5openalex-snapshot
Neural networks and complexity theory
inferred← citeshttps://doi.org/10.1007/3-540-60217-8_19openalex-snapshot
On lower bounds for the depth of threshold circuits with weights from {−1,0,+1}
inferred← citeshttps://doi.org/10.1007/978-1-4471-2097-1_121openalex-snapshot
On the Circuit Complexity of Feedforward Neural Networks
inferred← citeshttps://doi.org/10.1007/978-1-4615-2696-4_1openalex-snapshot
Neural Models and Spectral Methods
inferred← citeshttps://doi.org/10.1007/978-1-4615-2696-4_11openalex-snapshot
Learning Boolean Functions via the Fourier Transform
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/10.1007/978-3-031-78023-3_17openalex-snapshot
Reducing the Share Size of Weighted Threshold Secret Sharing Schemes via Chow Parameters Approximation
inferred← citeshttps://doi.org/10.1007/978-3-319-15612-5_27openalex-snapshot
Threshold Circuits for Global Patterns in 2-Dimensional Maps
inferred← citeshttps://doi.org/10.1007/978-3-540-45138-9_5openalex-snapshot
On the Computational Complexity of Conservative Computing
inferred← citeshttps://doi.org/10.1007/978-3-642-22935-0_54openalex-snapshot
Correlation Bounds for Poly-size $\mbox{\rm AC}^0$ Circuits with n 1 − o(1) Symmetric Gates
inferred← citeshttps://doi.org/10.1007/978-3-642-32512-0_29openalex-snapshot
Spectral Norm of Symmetric Functions
inferred← citeshttps://doi.org/10.1007/bf01200426openalex-snapshot
Majority gates vs. general weighted threshold gates
inferred← citeshttps://doi.org/10.1007/bf01206321openalex-snapshot
Pseudorandom generators and learning algorithms forAC 0
inferred← citeshttps://doi.org/10.1007/bf01268144openalex-snapshot
Evaluating spectral norms for constant depth circuits with symmetric gates
inferred← citeshttps://doi.org/10.1007/bfb0100486openalex-snapshot
Neural addition and fibonacci numbers
inferred← citeshttps://doi.org/10.1007/s00224-018-9900-3openalex-snapshot
Computing Majority by Constant Depth Majority Circuits with Low Fan-in Gates
inferred← citeshttps://doi.org/10.1007/s00493-013-2759-7openalex-snapshot
Optimal bounds for sign-representing the intersection of two halfspaces by polynomials
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)00159-1openalex-snapshot
A note on neural sorting networks with O(1) time complexity
inferred← citeshttps://doi.org/10.1016/0893-6080(94)90089-2openalex-snapshot
On the node complexity of neural networks
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/b978-0-444-89488-5.50133-0openalex-snapshot
Using Threshold Gates To Implement Sigmoid Nonlinearity
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/s0893-6080(02)00032-1openalex-snapshot
On the capabilities of neural networks using limited precision weights
inferred← citeshttps://doi.org/10.1016/s0893-6080(98)00039-2openalex-snapshot
Representations and rates of approximation of real-valued Boolean functions by neural networks
inferred← citeshttps://doi.org/10.1016/s0925-2312(97)00099-4openalex-snapshot
On the circuit and VLSI complexity of threshold gate comparison
inferred← citeshttps://doi.org/10.1017/cbo9781139814782openalex-snapshot
Analysis of Boolean Functions
inferred← citeshttps://doi.org/10.1038/s41467-025-58545-4openalex-snapshot
Unconditional advantage of noisy qudit quantum circuits over biased threshold circuits in constant depth
inferred← citeshttps://doi.org/10.1109/12.106225openalex-snapshot
Depth-size tradeoffs for neural computation
inferred← citeshttps://doi.org/10.1109/12.376175openalex-snapshot
On a constant-time, low-complexity winner-take-all neural network
inferred← citeshttps://doi.org/10.1109/12.537130openalex-snapshot
2-1 addition and related arithmetic operations with threshold logic
inferred← citeshttps://doi.org/10.1109/12.817389openalex-snapshot
Two operand binary adders with threshold logic
inferred← citeshttps://doi.org/10.1109/18.256500openalex-snapshot
Universal approximation bounds for superpositions of a sigmoidal function
inferred← citeshttps://doi.org/10.1109/18.256501openalex-snapshot
Depth efficient neural networks for division and related problems
inferred← citeshttps://doi.org/10.1109/18.312168openalex-snapshot
Rational approximation techniques for analysis of neural networks
inferred← citeshttps://doi.org/10.1109/18.312169openalex-snapshot
Lower bounds on threshold and related circuits via communication complexity
inferred← citeshttps://doi.org/10.1109/72.165601openalex-snapshot
A note on the complexity of reliability in neural networks
inferred← citeshttps://doi.org/10.1109/acssc.1991.186497openalex-snapshot
Circuit complexity for neural computation
inferred← citeshttps://doi.org/10.1109/ccc.2000.856756openalex-snapshot
On the complexity of quantum ACC
inferred← citeshttps://doi.org/10.1109/ccc.2012.37openalex-snapshot
Pseudorandom Generators for Read-Once ACC^0
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/iccad.1991.185211openalex-snapshot
LSAT-an algorithm for the synthesis of two level threshold gate networks
inferred← citeshttps://doi.org/10.1109/icnnsp.2003.1279280openalex-snapshot
Optimal practical adders using perceptrons
inferred← citeshttps://doi.org/10.1109/ijcnn.1992.226999openalex-snapshot
Maximizing the stability of a majority perceptron using Tabu search
inferred← citeshttps://doi.org/10.1109/ijcnn.1992.287222openalex-snapshot
Rational approximation, harmonic analysis and neural networks
inferred← citeshttps://doi.org/10.1109/ijcnn.2003.1223825openalex-snapshot
A survey of perceptron circuit complexity results
inferred← citeshttps://doi.org/10.1109/isit.1991.695142openalex-snapshot
Harmonic Analysis And The Complexity Of Computing With Threshold (Neural) Elements
inferred← citeshttps://doi.org/10.1109/isit.1993.748749openalex-snapshot
Constructions of Depth-2 Majority Circuits for Comparison and Addition using Linear Block Codes
inferred← citeshttps://doi.org/10.1109/isit45174.2021.9517872openalex-snapshot
Neural Network Computations with DOMINATION Functions
inferred← citeshttps://doi.org/10.1109/isit50566.2022.9834401openalex-snapshot
On Algebraic Constructions of Neural Networks with Small Weights
inferred← citeshttps://doi.org/10.1109/isit54713.2023.10206832openalex-snapshot
On the Information Capacity of Nearest Neighbor Representations
inferred← citeshttps://doi.org/10.1109/isnfs.1996.603834openalex-snapshot
VLSI complexity of threshold gate COMPARISON
inferred← citeshttps://doi.org/10.1109/nnsp.1992.253711openalex-snapshot
On the complexity of neural networks with sigmoidal units
inferred← citeshttps://doi.org/10.1109/sct.1992.215375openalex-snapshot
Majority gates vs. general weighted threshold gates
inferred← citeshttps://doi.org/10.1109/sfcs.1993.366838openalex-snapshot
A weak version of the Blum, Shub and Smale model
inferred← citeshttps://doi.org/10.1109/tc.2010.116openalex-snapshot
Exploring the Potential of Threshold Logic for Cryptography-Related Operations
inferred← citeshttps://doi.org/10.1109/tnnls.2024.3387878openalex-snapshot
Size and Depth of Monotone Neural Networks: Interpolation and Approximation
inferred← citeshttps://doi.org/10.1134/s0001434610050263openalex-snapshot
A small decrease in the degree of a polynomial with a given sign function can exponentially increase its weight and length
inferred← citeshttps://doi.org/10.1137/0221003openalex-snapshot
Polynomial Threshold Functions, $AC^0 $ Functions, and Spectral Norms
inferred← citeshttps://doi.org/10.1137/0222080openalex-snapshot
Learning Decision Trees Using the Fourier Spectrum
inferred← citeshttps://doi.org/10.1137/08071421xopenalex-snapshot
Separating ${AC}^0$ from Depth-2 Majority Circuits
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/10.1137/s0895480191218496openalex-snapshot
Explicit Constructions of Depth-2 Majority Circuits for Comparison and Addition
inferred← citeshttps://doi.org/10.1137/s0895480192228619openalex-snapshot
On Optimal Depth Threshold Circuits for Multiplication and Related Problems
inferred← citeshttps://doi.org/10.1137/s0895480193244292openalex-snapshot
On the Power of Democratic Networks
inferred← citeshttps://doi.org/10.1137/s0895480197326048openalex-snapshot
Algebraic Techniques for Constructing Minimal Weight Threshold Functions
inferred← citeshttps://doi.org/10.1142/s0129065796000129openalex-snapshot
CONSTRUCTIVE TRAINING METHODS FOR FEEDFORWARD NEURAL NETWORKS WITH BINARY WEIGHTS
inferred← citeshttps://doi.org/10.1145/103418.103466openalex-snapshot
Learning decision trees using the Fourier spectrum
inferred← citeshttps://doi.org/10.1145/1250790.1250834openalex-snapshot
Separating AC 0 from depth-2 majority circuits
inferred← citeshttps://doi.org/10.1145/130385.130391openalex-snapshot
An O(n log log n ) learning algorithm for DNF under the uniform distribution
inferred← citeshttps://doi.org/10.1145/130385.130392openalex-snapshot
A technique for upper bounding the spectral norm with applications to learning
inferred← citeshttps://doi.org/10.1145/130385.130416openalex-snapshot
On learning noisy threshold functions with finite precision weights
inferred← citeshttps://doi.org/10.1145/167088.167234openalex-snapshot
Simulating threshold circuits by majority circuits
inferred← citeshttps://doi.org/10.1145/195058.195236openalex-snapshot
Pseudorandom generators and learning algorithms for AC
inferred← citeshttps://doi.org/10.1145/3055399.3055425openalex-snapshot
Addition is exponentially harder than counting for shallow monotone circuits
inferred← citeshttps://doi.org/10.1145/3055399.3055470openalex-snapshot
A polynomial restriction lemma with applications
inferred← citeshttps://doi.org/10.1145/3409964.3461813openalex-snapshot
Provable Advantages for Graph Algorithms in Spiking Neural Networks
inferred← citeshttps://doi.org/10.1162/neco_a_01683openalex-snapshot
Trade-Offs Between Energy and Depth of Neural Networks
inferred← citeshttps://doi.org/10.1587/transfun.e101.a.1431openalex-snapshot
Computational Power of Threshold Circuits of Energy at most Two
inferred← citeshttps://doi.org/10.2139/ssrn.5088079openalex-snapshot
Threshold Circuit Based On Cyclic Codes
inferred← citeshttps://doi.org/10.26421/qic2.1-3openalex-snapshot
Counting, fanout and the complexity of quantum ACC
inferred← citeshttps://doi.org/10.4213/mzm8734openalex-snapshot
Небольшое уменьшение степени многочлена с заданной знаковой функцией может экспоненциально увеличить его вес и длину
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.mfcs.2023.85openalex-snapshot
Exponential Lower Bounds for Threshold Circuits of Sub-Linear Depth and Energy
inferred← citeshttps://doi.org/10.4324/9781315784076-105openalex-snapshot
Placing Feedforward Neural Networks Among Several Circuit Complexity Classes
inferred← citeshttps://doi.org/10.48550/arxiv.1205.5282openalex-snapshot
Spectral Norm of Symmetric Functions
inferred← citeshttps://doi.org/10.48550/arxiv.quant-ph/0106017openalex-snapshot
Counting, Fanout, and the Complexity of Quantum ACC
inferred← citeshttps://doi.org/10.5555/114836.114846openalex-snapshot
A geometric approach to threshold circuit complexity
inferred← citeshttps://doi.org/10.7155/jgaa.00387openalex-snapshot
Threshold Circuits Detecting Global Patterns in Two-dimensional Maps
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