On the public citation graph
On the power of small-depth threshold circuits
0 signed · 238 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
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/bf01744431openalex-snapshot
Parity, circuits, and the polynomial-time hierarchy
inferredcites →https://doi.org/10.1007/bf02579196openalex-snapshot
The monotone circuit complexity of boolean functions
inferredcites →https://doi.org/10.1016/0168-0072(83)90038-6openalex-snapshot
∑11-Formulae on finite structures
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/sfcs.1984.715894openalex-snapshot
Log Depth Circuits For Division And Related Problems
inferredcites →https://doi.org/10.1109/sfcs.1985.49openalex-snapshot
Separating the polynomial-time hierarchy by oracles
inferredcites →https://doi.org/10.1109/sfcs.1987.59openalex-snapshot
Threshold circuits of bounded depth
inferredcites →https://doi.org/10.1109/sfcs.1989.63527openalex-snapshot
On the computational power of PP and (+)P
inferredcites →https://doi.org/10.1109/sfcs.1989.63535openalex-snapshot
Probabilistic communication complexity of Boolean relations
inferredcites →https://doi.org/10.1109/sfcs.1989.63538openalex-snapshot
A note on the power of threshold circuits
inferredcites →https://doi.org/10.1137/0217015openalex-snapshot
Unbiased Bits from Sources of Weak Randomness and Probabilistic Communication Complexity
inferredcites →https://doi.org/10.1145/100216.100253openalex-snapshot
Monotone circuits for matching require linear depth
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/10.1145/62212.62265openalex-snapshot
Monotone circuits for connectivity require super-logarithmic depth
inferredcites →https://doi.org/10.1145/73007.73008openalex-snapshot
Multiparty protocols and logspace-hard pseudorandom sequences
inferredcites →https://doi.org/10.1145/800061.808733openalex-snapshot
Borel sets and circuit complexity
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781119162254.ch06openalex-snapshot
Dynamical Systems and Interaction Networks
inferred← citeshttps://doi.org/10.1002/ima.22377openalex-snapshot
Hybrid fully convolutional networks‐based skin lesion segmentation and melanoma detection using deep feature
inferred← citeshttps://doi.org/10.1006/jcss.1997.1552openalex-snapshot
A Lower Bound for Perceptrons and an Oracle Separation of the PPPHHierarchy
inferred← citeshttps://doi.org/10.1007/10719839_21openalex-snapshot
Improved Upper Bounds on the Simultaneous Messages Complexity of the Generalized Addressing Function
inferred← citeshttps://doi.org/10.1007/11523468_80openalex-snapshot
Lower Bounds for Circuits with Few Modular and Symmetric Gates
inferred← citeshttps://doi.org/10.1007/11523468_94openalex-snapshot
Hadamard Tensors and Lower Bounds on Multiparty Communication Complexity
inferred← citeshttps://doi.org/10.1007/11821069_34openalex-snapshot
On the Correlation Between Parity and Modular Polynomials
inferred← citeshttps://doi.org/10.1007/3-540-55808-x_5openalex-snapshot
Neural networks and complexity theory
inferred← citeshttps://doi.org/10.1007/3-540-59042-0_88openalex-snapshot
Simultaneous messages vs. communication
inferred← citeshttps://doi.org/10.1007/3-540-60922-9_52openalex-snapshot
Some bounds on multiparty communication complexity of pointer jumping
inferred← citeshttps://doi.org/10.1007/3-540-61332-3_147openalex-snapshot
Harmonic analysis, real approximation, and the communication complexity of Boolean functions
inferred← citeshttps://doi.org/10.1007/3-540-61332-3_156openalex-snapshot
Depth-efficient threshold circuits for multiplication and symmetric function computation
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/10.1007/978-1-4419-9675-6_12openalex-snapshot
Noise Sensitivity of Boolean Functions and Applications to Percolation
inferred← citeshttps://doi.org/10.1007/978-1-4471-5571-3_2openalex-snapshot
Fundamentals of Machine Learning
inferred← citeshttps://doi.org/10.1007/978-1-4612-1872-2_11openalex-snapshot
Polynomials and Combinatorial Definitions of Languages
inferred← citeshttps://doi.org/10.1007/978-1-4614-8094-5_22openalex-snapshot
Dendrites Enhance Both Single Neuron and Network Computation
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/10.1007/978-3-030-26176-4_43openalex-snapshot
On the Relationship Between Energy Complexity and Other Boolean Function Measures
inferred← citeshttps://doi.org/10.1007/978-3-030-36030-6_16openalex-snapshot
The Function-Inversion Problem: Barriers and Opportunities
inferred← citeshttps://doi.org/10.1007/978-3-032-29760-0_8openalex-snapshot
Circuit Complexity of Hierarchical Knowledge Tracing
inferred← citeshttps://doi.org/10.1007/978-3-319-25159-2_49openalex-snapshot
Building Program Vector Representations for Deep Learning
inferred← citeshttps://doi.org/10.1007/978-3-319-46493-0_39openalex-snapshot
Deep Networks with Stochastic Depth
inferred← citeshttps://doi.org/10.1007/978-3-322-95233-2_15openalex-snapshot
Communication Complexity and Lower Bounds for Sequential Computation
inferred← citeshttps://doi.org/10.1007/978-3-540-28629-5_24openalex-snapshot
Some Meet-in-the-Middle Circuit Lower Bounds
inferred← citeshttps://doi.org/10.1007/978-3-540-45138-9_5openalex-snapshot
On the Computational Complexity of Conservative Computing
inferred← citeshttps://doi.org/10.1007/978-3-540-72504-6_28openalex-snapshot
Quantum Multiparty Communication Complexity and Circuit Lower Bounds
inferred← citeshttps://doi.org/10.1007/978-3-540-73420-8_14openalex-snapshot
Separating Deterministic from Nondeterministic NOF Multiparty Communication Complexity
inferred← citeshttps://doi.org/10.1007/978-3-540-85363-3_30openalex-snapshot
Improved Separations between Nondeterministic and Randomized Multiparty Communication
inferred← citeshttps://doi.org/10.1007/978-3-540-85780-8_7openalex-snapshot
On the Non-deterministic Communication Complexity of Regular Languages
inferred← citeshttps://doi.org/10.1007/978-3-642-03351-3_13openalex-snapshot
Depth Reduction for Circuits with a Single Layer of Modular Counting Gates
inferred← citeshttps://doi.org/10.1007/978-3-642-03409-1_30openalex-snapshot
Energy Complexity and Depth of Threshold Circuits
inferred← citeshttps://doi.org/10.1007/978-3-642-20877-5_16openalex-snapshot
Energy and Fan-In of Threshold Circuits Computing Mod Functions
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/10.1007/978-3-642-22993-0_51openalex-snapshot
Lower Bounds for Linear Decision Trees via an Energy Complexity Argument
inferred← citeshttps://doi.org/10.1007/978-3-642-24412-4_3openalex-snapshot
On the Expressive Power of Deep Architectures
inferred← citeshttps://doi.org/10.1007/978-3-642-24477-3_1openalex-snapshot
On the Expressive Power of Deep Architectures
inferred← citeshttps://doi.org/10.1007/978-3-642-24508-4openalex-snapshot
Boolean Function Complexity: Advances and Frontiers
inferred← citeshttps://doi.org/10.1007/978-3-642-31594-7_2openalex-snapshot
The NOF Multiparty Communication Complexity of Composed Functions
inferred← citeshttps://doi.org/10.1007/978-3-642-35289-8_26openalex-snapshot
Practical Recommendations for Gradient-Based Training of Deep Architectures
inferred← citeshttps://doi.org/10.1007/978-3-642-36657-4_1openalex-snapshot
Deep Learning of Representations
inferred← citeshttps://doi.org/10.1007/978-3-642-40313-2_46openalex-snapshot
Polynomial Threshold Functions and Boolean Threshold Circuits
inferred← citeshttps://doi.org/10.1007/978-3-642-55337-0_3openalex-snapshot
Evolving Culture Versus Local Minima
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/10.1007/978-981-96-0668-9_20openalex-snapshot
A Satisfiability Algorithm for Depth Two Circuits with a Sub-Quadratic Number of Symmetric and Threshold Gates
inferred← citeshttps://doi.org/10.1007/bf01185398openalex-snapshot
A lower bound for monotone perceptrons
inferred← citeshttps://doi.org/10.1007/bf01294257openalex-snapshot
Upper and lower bounds for some depth-3 circuit classes
inferred← citeshttps://doi.org/10.1007/bf02698830openalex-snapshot
Noise sensitivity of boolean functions and applications to percolation
inferred← citeshttps://doi.org/10.1007/pl00001592openalex-snapshot
Circuits and multi-party protocols
inferred← citeshttps://doi.org/10.1007/pl00001602openalex-snapshot
The BNS-Chung criterion for multi-party communication complexity
inferred← citeshttps://doi.org/10.1007/s00037-009-0276-2openalex-snapshot
Disjointness is Hard in the Multiparty Number-on-the-Forehead Model
inferred← citeshttps://doi.org/10.1007/s00037-012-0052-6openalex-snapshot
Hadamard tensors and lower bounds on multiparty communication complexity
inferred← citeshttps://doi.org/10.1007/s00037-013-0078-4openalex-snapshot
The NOF Multiparty Communication Complexity of Composed Functions
inferred← citeshttps://doi.org/10.1007/s00224-011-9332-9openalex-snapshot
On the Correlation Between Parity and Modular Polynomials
inferred← citeshttps://doi.org/10.1007/s002240000126openalex-snapshot
Exponential Sums and Circuits with a Single Threshold Gate and Mod-Gates
inferred← citeshttps://doi.org/10.1007/s00493-009-2667-zopenalex-snapshot
One-way multiparty communication lower bound for pointer jumping with applications
inferred← citeshttps://doi.org/10.1007/s00493-014-3078-3openalex-snapshot
The communication complexity of addition
inferred← citeshttps://doi.org/10.1007/s004930100009openalex-snapshot
The Cost of the Missing Bit: Communication Complexity with Help
inferred← citeshttps://doi.org/10.1007/s00521-023-08708-5openalex-snapshot
An intelligence parameter classification approach for energy storage and natural convection and heat transfer of nano-encapsulated phase change material: Deep neural networks
inferred← citeshttps://doi.org/10.1007/s10462-022-10213-5openalex-snapshot
A review of convolutional neural network architectures and their optimizations
inferred← citeshttps://doi.org/10.1007/s10878-020-00689-8openalex-snapshot
On the relationship between energy complexity and other boolean function measures
inferred← citeshttps://doi.org/10.1007/s10973-020-09875-6openalex-snapshot
Using deep learning to learn physics of conduction heat transfer
inferred← citeshttps://doi.org/10.1007/s13042-013-0198-9openalex-snapshot
Bi-firing deep neural networks
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(94)00221-jopenalex-snapshot
A note on the power of majority gates and modular gates
inferred← citeshttps://doi.org/10.1016/0022-0000(92)90047-mopenalex-snapshot
Multiparty protocols, pseudorandom generators for logspace, and time-space trade-offs
inferred← citeshttps://doi.org/10.1016/b978-0-12-824447-0.00013-3openalex-snapshot
Tensors for deep learning theory
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.ic.2014.09.008openalex-snapshot
Polynomial threshold functions and Boolean threshold circuits
inferred← citeshttps://doi.org/10.1016/j.inffus.2017.03.008openalex-snapshot
On building ensembles of stacked denoising auto-encoding classifiers and their further improvement
inferred← citeshttps://doi.org/10.1016/j.ipl.2018.07.002openalex-snapshot
A note on multiparty communication complexity and the Hales–Jewett theorem
inferred← citeshttps://doi.org/10.1016/j.jcss.2004.01.003openalex-snapshot
The correlation between parity and quadratic polynomials mod3
inferred← citeshttps://doi.org/10.1016/j.jcss.2019.04.004openalex-snapshot
Bounded depth circuits with weighted symmetric gates: Satisfiability, lower bounds and compression
inferred← citeshttps://doi.org/10.1016/j.neunet.2022.01.012openalex-snapshot
Low-degree term first in ResNet, its variants and the whole neural network family
inferred← citeshttps://doi.org/10.1016/j.neunet.2022.12.016openalex-snapshot
Tucker network: Expressive power and comparison
inferred← citeshttps://doi.org/10.1016/j.tcs.2008.07.028openalex-snapshot
Exponential lower bounds on the size of constant-depth threshold circuits with small energy complexity
inferred← citeshttps://doi.org/10.1016/j.tcs.2010.08.006openalex-snapshot
Energy and depth of threshold circuits
inferred← citeshttps://doi.org/10.1016/j.tcs.2012.11.039openalex-snapshot
Energy and fan-in of logic circuits computing symmetric Boolean functions
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/s0020-0190(97)00141-5openalex-snapshot
On the power of a threshold gate at the top
inferred← citeshttps://doi.org/10.1016/s0065-2458(08)60342-3openalex-snapshot
Communication Complexity
inferred← citeshttps://doi.org/10.1016/s0196-6774(02)00204-3openalex-snapshot
Constructing set systems with prescribed intersection sizes
inferred← citeshttps://doi.org/10.1016/s0304-3975(96)00290-3openalex-snapshot
On the power of circuits with gates of low L1 norms
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/10.1017/9781009025096.004openalex-snapshot
Expressivity of Deep Neural Networks
inferred← citeshttps://doi.org/10.1017/9781009025096.007openalex-snapshot
Stochastic Feedforward Neural Networks: Universal Approximation
inferred← citeshttps://doi.org/10.1017/s0960129508007263openalex-snapshot
Quantum multiparty communication complexity and circuit lower bounds
inferred← citeshttps://doi.org/10.1070/rm2003v058n05abeh000667openalex-snapshot
Monotone Boolean functions
inferred← citeshttps://doi.org/10.1109/ccc.1997.612310openalex-snapshot
Upper and lower bounds for some depth-3 circuit classes
inferred← citeshttps://doi.org/10.1109/ccc.2001.933885openalex-snapshot
Lower bounds for approximations by low degree polynomials over Z/sub m/
inferred← citeshttps://doi.org/10.1109/ccc.2002.1004341openalex-snapshot
The correlation between parity and quadratic polynomials mod 3
inferred← citeshttps://doi.org/10.1109/ccc.2005.25openalex-snapshot
Pseudorandom Bits for Constant Depth Circuits with Few Arbitrary Symmetric Gates
inferred← citeshttps://doi.org/10.1109/ccc.2007.14openalex-snapshot
Lower Bounds for Multi-Player Pointer Jumping
inferred← citeshttps://doi.org/10.1109/ccc.2007.15openalex-snapshot
Norms, XOR Lemmas, and Lower Bounds for GF(2) Polynomials and Multiparty Protocols
inferred← citeshttps://doi.org/10.1109/ccc.2007.4openalex-snapshot
An Exponential Lower Bound on the Size of Constant-Depth Threshold Circuits with Small Energy Complexity
inferred← citeshttps://doi.org/10.1109/ccc.2008.29openalex-snapshot
Disjointness Is Hard in the Multi-party Number-on-the-Forehead Model
inferred← citeshttps://doi.org/10.1109/ccc.2009.24openalex-snapshot
Lower Bounds on Quantum Multiparty Communication Complexity
inferred← citeshttps://doi.org/10.1109/ccc.2009.30openalex-snapshot
The Maximum Communication Complexity of Multi-Party Pointer Jumping
inferred← citeshttps://doi.org/10.1109/cit.2014.144openalex-snapshot
Feature Extraction and Pattern Recognition for Human Motion by a Deep Sparse Autoencoder
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00466openalex-snapshot
Learning Strict Identity Mappings in Deep Residual Networks
inferred← citeshttps://doi.org/10.1109/focs.2007.24openalex-snapshot
Computing Equilibria in Anonymous Games
inferred← citeshttps://doi.org/10.1109/focs.2007.30openalex-snapshot
Discrepancy and the Power of Bottom Fan-in in Depth-three Circuits
inferred← citeshttps://doi.org/10.1109/focs.2007.4389513openalex-snapshot
One-Way Multi-Party Communication Lower Bound for Pointer Jumping with Applications
inferred← citeshttps://doi.org/10.1109/focs.2007.51openalex-snapshot
A Brief Look at Pairings Based Cryptography
inferred← citeshttps://doi.org/10.1109/focs.2009.12openalex-snapshot
Multiparty Communication Complexity and Threshold Circuit Size of AC^0
inferred← citeshttps://doi.org/10.1109/focs.2019.00045openalex-snapshot
Leakage-Resilient Secret Sharing Against Colluding Parties
inferred← citeshttps://doi.org/10.1109/focs46700.2020.00117openalex-snapshot
Extractors and Secret Sharing Against Bounded Collusion Protocols
inferred← citeshttps://doi.org/10.1109/icmlc.2014.7009128openalex-snapshot
Auto-encoder using the bi-firing activation function
inferred← citeshttps://doi.org/10.1109/icpr.2016.7900170openalex-snapshot
Deep networks are efficient for circular manifolds
inferred← citeshttps://doi.org/10.1109/ictai.2019.00110openalex-snapshot
Reconciling Feature-Reuse and Overfitting in DenseNet with Specialized Dropout
inferred← citeshttps://doi.org/10.1109/mecatronics.2018.8495837openalex-snapshot
Fast and High-performance Multi-convolution Deep Neural Network Structure with Residuals
inferred← citeshttps://doi.org/10.1109/sfcs.1992.267764openalex-snapshot
Separating the communication complexities of MOD m and MOD p circuits
inferred← citeshttps://doi.org/10.1109/sfcs.1994.365685openalex-snapshot
A lower bound for the monotone depth of connectivity
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/10.1109/tbme.2014.2372011openalex-snapshot
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
inferred← citeshttps://doi.org/10.1109/tmi.2016.2642839openalex-snapshot
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
inferred← citeshttps://doi.org/10.1109/tmi.2017.2787657openalex-snapshot
SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network
inferred← citeshttps://doi.org/10.1109/tnnls.2013.2293637openalex-snapshot
On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures
inferred← citeshttps://doi.org/10.1109/tnnls.2013.2296046openalex-snapshot
Deep Networks are Effective Encoders of Periodicity
inferred← citeshttps://doi.org/10.1109/tpami.2013.50openalex-snapshot
Representation Learning: A Review and New Perspectives
inferred← citeshttps://doi.org/10.1111/j.1467-8640.2010.00366.xopenalex-snapshot
DECISION TREES DO NOT GENERALIZE TO NEW VARIATIONS
inferred← citeshttps://doi.org/10.1137/050640941openalex-snapshot
Pseudorandom Bits for Constant‐Depth Circuits with Few Arbitrary Symmetric Gates
inferred← citeshttps://doi.org/10.1137/080735096openalex-snapshot
Hardness Amplification Proofs Require Majority
inferred← citeshttps://doi.org/10.1137/1.9781611973105.46openalex-snapshot
The communication complexity of addition
inferred← citeshttps://doi.org/10.1137/100792779openalex-snapshot
Multiparty Communication Complexity and Threshold Circuit Size of $\ensuremath{\sfAC}^0$
inferred← citeshttps://doi.org/10.1137/120891587openalex-snapshot
The Multiparty Communication Complexity of Set Disjointness
inferred← citeshttps://doi.org/10.1137/21m1413699openalex-snapshot
Sharp Bounds for the Number of Regions of Maxout Networks and Vertices of Minkowski Sums
inferred← citeshttps://doi.org/10.1137/21m1434076openalex-snapshot
Globally Convergent Multilevel Training of Deep Residual Networks
inferred← citeshttps://doi.org/10.1137/s0097539700375944openalex-snapshot
Communication Complexity of Simultaneous Messages
inferred← citeshttps://doi.org/10.1137/s0097539794264809openalex-snapshot
Boolean Circuits, Tensor Ranks, and Communication Complexity
inferred← citeshttps://doi.org/10.1137/s0097539794274519openalex-snapshot
Simulating Threshold Circuits by Majority Circuits
inferred← citeshttps://doi.org/10.1137/s0895480192228619openalex-snapshot
On Optimal Depth Threshold Circuits for Multiplication and Related Problems
inferred← citeshttps://doi.org/10.1142/s0129054110007386openalex-snapshot
ON THE NON-DETERMINISTIC COMMUNICATION COMPLEXITY OF REGULAR LANGUAGES
inferred← citeshttps://doi.org/10.1145/103418.103463openalex-snapshot
Rounds in communication complexity revisited
inferred← citeshttps://doi.org/10.1145/1374376.1374472openalex-snapshot
Communication in the presence of replication
inferred← citeshttps://doi.org/10.1145/1577069.1577070openalex-snapshot
Exploring Strategies for Training Deep Neural Networks
inferred← citeshttps://doi.org/10.1145/1595391.1595392openalex-snapshot
Improved Separations between Nondeterministic and Randomized Multiparty Communication
inferred← citeshttps://doi.org/10.1145/167088.167234openalex-snapshot
Simulating threshold circuits by majority circuits
inferred← citeshttps://doi.org/10.1145/2213977.2214026openalex-snapshot
The multiparty communication complexity of set disjointness
inferred← citeshttps://doi.org/10.1145/2488608.2488725openalex-snapshot
Communication lower bounds using directional derivatives
inferred← citeshttps://doi.org/10.1145/2611462.2611493openalex-snapshot
On the power of the congested clique model
inferred← citeshttps://doi.org/10.1145/2629334openalex-snapshot
Communication Lower Bounds Using Directional Derivatives
inferred← citeshttps://doi.org/10.1145/3055399.3055425openalex-snapshot
Addition is exponentially harder than counting for shallow monotone circuits
inferred← citeshttps://doi.org/10.1145/3061640.3061648openalex-snapshot
Selected challenges in computational lower bounds
inferred← citeshttps://doi.org/10.1145/3293611.3331625openalex-snapshot
Hardness of Exact Distance Queries in Sparse Graphs Through Hub Labeling
inferred← citeshttps://doi.org/10.1145/3564246.3585216openalex-snapshot
Depth-𝑑 Threshold Circuits vs. Depth-(𝑑+1) AND-OR Trees
inferred← citeshttps://doi.org/10.1162/neco.1996.8.4.805openalex-snapshot
Analog versus Discrete Neural Networks
inferred← citeshttps://doi.org/10.1162/neco.2010.08-09-1081openalex-snapshot
Deep Belief Networks Are Compact Universal Approximators
inferred← citeshttps://doi.org/10.1162/neco_a_01683openalex-snapshot
Trade-Offs Between Energy and Depth of Neural Networks
inferred← citeshttps://doi.org/10.1287/educ.2019.0201openalex-snapshot
Deep Learning in Computer Vision: Methods, Interpretation, Causation, and Fairness
inferred← citeshttps://doi.org/10.1504/ijdmb.2016.076534openalex-snapshot
A protein-protein interaction extraction approach based on deep neural network
inferred← citeshttps://doi.org/10.1561/0400000033openalex-snapshot
On the Power of Small-Depth Computation
inferred← citeshttps://doi.org/10.1561/0400000040openalex-snapshot
Lower Bounds in Communication Complexity
inferred← citeshttps://doi.org/10.1561/2000000071openalex-snapshot
Deep Learning in Object Recognition, Detection, and Segmentation
inferred← citeshttps://doi.org/10.1561/9781601982599openalex-snapshot
Lower Bounds in Communication Complexity
inferred← citeshttps://doi.org/10.1561/9781601982957openalex-snapshot
Learning Deep Architectures for AI
inferred← citeshttps://doi.org/10.1561/9781601983015openalex-snapshot
On the Power of Small-Depth Computation
inferred← citeshttps://doi.org/10.19086/da.28933openalex-snapshot
Larger Corner-Free Sets from Better NOF Exactly-$N$ Protocols
inferred← citeshttps://doi.org/10.20944/preprints202008.0641.v2openalex-snapshot
Brain Tumor Detection based on Ensemble Learning
inferred← citeshttps://doi.org/10.21629/jsee.2018.04.22openalex-snapshot
Application of deep autoencoder model for structural condition monitoring
inferred← citeshttps://doi.org/10.22214/ijraset.2018.3522openalex-snapshot
Design and Implementation of CNN for detecting Melanoma through Image Processing
inferred← citeshttps://doi.org/10.3389/fncom.2018.00100openalex-snapshot
Bio-inspired Analysis of Deep Learning on Not-So-Big Data Using Data-Prototypes
inferred← citeshttps://doi.org/10.3390/math11224651openalex-snapshot
A Note on the LogRank Conjecture in Communication Complexity
inferred← citeshttps://doi.org/10.3390/rs11202454openalex-snapshot
A Superpixel-Based Relational Auto-Encoder for Feature Extraction of Hyperspectral Images
inferred← citeshttps://doi.org/10.4036/iis.2014.27openalex-snapshot
Lower Bounds for Threshold Circuits of Bounded Energy
inferred← citeshttps://doi.org/10.4216/jpssj.50.0_51openalex-snapshot
How and What Deep Learning Learns
inferred← citeshttps://doi.org/10.4230/lipics.approx-random.2015.606openalex-snapshot
Dependent Random Graphs and Multi-Party Pointer Jumping
inferred← citeshttps://doi.org/10.4230/lipics.approx-random.2018.56openalex-snapshot
Improved Pseudorandom Generators from Pseudorandom Multi-Switching Lemmas
inferred← citeshttps://doi.org/10.4230/lipics.itcs.2019.54openalex-snapshot
On The Communication Complexity of High-Dimensional Permutations
inferred← citeshttps://doi.org/10.4230/lipics.itcs.2019.8openalex-snapshot
A #SAT Algorithm for Small Constant-Depth Circuits with PTF Gates
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/10.4230/lipics.stacs.2008.1341openalex-snapshot
Sublinear Communication Protocols for Multi-Party Pointer Jumping and a Related Lower Bound
inferred← citeshttps://doi.org/10.48550/arxiv.1004.0817openalex-snapshot
A Separation of NP and coNP in Multiparty Communication Complexity
inferred← citeshttps://doi.org/10.48550/arxiv.1203.2990openalex-snapshot
Evolving Culture vs Local Minima
inferred← citeshttps://doi.org/10.48550/arxiv.1206.5538openalex-snapshot
Representation Learning: A Review and New Perspectives
inferred← citeshttps://doi.org/10.48550/arxiv.1207.4404openalex-snapshot
Better Mixing via Deep Representations
inferred← citeshttps://doi.org/10.48550/arxiv.1211.1544openalex-snapshot
Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds
inferred← citeshttps://doi.org/10.48550/arxiv.1311.5467openalex-snapshot
Challenges in computational lower bounds
inferred← citeshttps://doi.org/10.48550/arxiv.1312.6098openalex-snapshot
On the number of response regions of deep feed forward networks with piece-wise linear activations
inferred← citeshttps://doi.org/10.48550/arxiv.1409.3358openalex-snapshot
Building Program Vector Representations for Deep Learning
inferred← citeshttps://doi.org/10.48550/arxiv.1412.2667openalex-snapshot
Computing Exact Distances in the Congested Clique
showing the first 200 of 218 · page through all 218
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.