[153] Accelerated Sampling with Stacked Restricted Boltzmann Machines
J. Fernandez de Cossio Diaz, C. Roussel, S. Cocco, R. Monasson
Twelth Conference on International Conference on Learning Representations - ICLR (2024)

[152] Origins and breadth of pairwise epistasis in an alpha-helix of beta-lactamase TEM-1
A. Birgy, C. Roussel, H. Kemble, J. Mullaert, K. Panigoni, A. Chapron, J. Chatel, M. Magnan, H. Jacquier, S. Cocco, R. Monasson, O. Tenaillon
submitted (2023)

[151] Minimal epistatic networks from integrated sequence and mutational protein data
S.Cocco, L. Posani, R. Monasson
submitted (2023)

[150] Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment
C. Malbranke, W. Rostain, F. Depardieu, S.Cocco, R. Monasson, D. Bikard
PLoS Computational Biology 19:e1011621 (2023)

[149] Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivity
T. Kuehn, R. Monasson
Phys. Rev. E 108, 064301 (2023)

[148] Replica method for computational problems with randomness: principles and illustrations
J. Steinberg, U. Adomaityte, A. Fachechi, P. Mergny, D. Barbier, R. Monasson
Lecture notes from the Les Houches Summer School 2022; To Appear in SciPost Phys. Lect. Notes (2023)

[147] Transition paths in Potts-like energy landscapes: General properties and application to protein sequence models
E. Mauri, S. Cocco, R. Monasson
Phys. Rev. E 108, 024141 (2023)

[146] Infer global, predict local: quantity-relevance trade-off in protein fitness predictions from sequence data
L. Posani, F. Rizzato, R. Monasson, S. Cocco
PLoS Computational Biology 19:e1011521 (2023)

[145] A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity
B. Bravi, A. Di Gioacchino, J. Fernandez de Cossio Diaz, A. Walczak, T. Mora, S. Cocco, R. Monasson
eLife 12:e85126 (2023)

[144] Evolutionary Dynamics of a Lattice Dimer: a Toy Model for Stability vs. Affinity Trade-offs in Proteins
E. Loffredo, E. Vesconi, R. Razban, O. Peleg, E. Shakhnovich, S. Cocco, R. Monasson
J. Phys. A 56 455002 (2023); Special issue on Random Landscapes and Dynamics in Evolution, Ecology and Beyond

[143] Repeats Mimic Immunostimulatory Viral Features Across a Vast Evolutionary Landscape
P. Sulc, A. Di Gioacchino, A. Solovyov, S.A. Marhon, S. Sun, H.T. Lindholm, R. Chen, A. Hosseini, H. Jiang, B.H. Li, P. Mehdipour, O. Abdel-Wahab, N. Vabret, K. LaCava, D. De Carvahlo, R. Monasson, S. Cocco, B.D. Greenbaum
submitted for publication (2023)

[142] Disentangling representations in Restricted Boltzmann Machines without adversaries
J. Fernandez-de-Cossio-Diaz, S. Cocco, R. Monasson
Physical Review X 13, 021003 (2023)

[141] Mutational paths in protein-sequence landscapes: from sampling to mean-field characterization
E. Mauri, S. Cocco, R. Monasson
Physical Review Letters 130, 158402 (2023)

[140] Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies
C. Malbranke, D. Bikard, S. Cocco, R. Monasson, J. Tubiana
Current Opinion in Structural Biology 80:102571 (2023)

[139] Emergence of time persistence in a data-driven neural network model
S. Wolf, G. Le Goc, S. Cocco, G. Debregeas, R. Monasson
eLife 12:e79541 (2023)

[138] Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
A. Di Gioacchino, J. Procyk, M. Molari, J.S. Schreck, Y. Zou, Y. Liu, R. Monasson, S. Cocco, P. Sulc
PLoS Comp. Bio. 18(9):e1010561 (2022)

[137] Neoantigen quality predicts immunoediting in pancreatic cancer survivors
M. Luksza, Z.M. Sethna, L.A. Rojas, J. Lihm, B. Bravi, Y. Elhanati, K. Soares, M. Amisaki, D. Hoyos, A. Dobrin, P. Guasp, A. Zebboudj, R. Yu, A.K. Chandra, T. Waters, Z. Odgerel, J. Leung, R. Kappagantula, A. Makohon-Moore, A. Johns, A. Gill, M. Gigoux, J. Wolchok, T. Merghoub, M. Sadelain, E. Patterson, C. Iacobuzio-Donahue, R. Monasson, T. Mora, A.M. Walczak, S. Cocco, B.D. Greenbaum, V.P. Balachandran
Nature 606, 389-395 (2022)

[136] Optimal regularizations for data generation with probabilistic graphical models
A. Fanthomme, F. Rizzato, S. Cocco, R. Monasson
J. Stat. Mech. 053502 (2022)

[135] Barriers and dynamical paths in alternating Gibbs sampling of restricted Boltzmann machines
C. Roussel, S. Cocco, R. Monasson
Physical Review E 104, 034109 (2021)

[134] Inferring epistasis from genomic data with comparable mutation and outcrossing rate
H-L. Zeng, E. Mauri, V. Dichio, S. Cocco, R. Monasson, E. Aurell
J. Stat. Mech. 083501 (2021)

[133] A synaptic novelty signal in the dentate gyrus supports switching hippocampal attractor networks from generalization to discrimination
R. Gomez-Ocadiz, M. Trippa, L. Posani, S. Cocco, R. Monasson, C. Schmidt-Hieber
Nature Communications 13, 4122 (2022)

[132] Probing T-cell response by sequence-based probabilistic modeling
B. Bravi, V.P. Balachandran, B.D. Greenbaum, A.M. Walczak, T. Mora, R. Monasson, S. Cocco
PLoS Computational Biology 17(9): e1009297 (2021)

[131] Survival probability and size of lineages in antibody affinity maturation
M. Molari, R. Monasson, S. Cocco
Physical Review E 103, 052413 (2021)

[130] Improving sequence-based modeling of protein families using secondary structure quality assessment
C. Malbranke, D. Bikard, S. Cocco, R. Monasson
Bioinformatics, btab442 (2021)

[129] Low-Dimensional Manifolds Support Multiplexed Integrations in Recurrent Neural Networks
A. Fanthomme, R. Monasson
Neural Computation 33, 1-50 (2021)

[128] Gaussian Closure Scheme in the Quasi-Linkage Equilibrium Regime of Evolving Genome Populations
E. Mauri, S. Cocco, R. Monasson
Europhysics Letters 132, 56001 (2020)

[127] The heterogeneous landscape and early evolution of pathogen-associated CpG dinucleotides in SARS-CoV-2
A. Di Gioacchino, P. Sulc, A.V. Komarova, B.D. Greenbaum, R. Monasson, S. Cocco
Molecular Biology and Evolution 38, 2428-2445 (2021)

[126] RBM-MHC: a semi-supervised machine-learning method for sample-specific prediction of antigen presentation by HLA-I alleles
B. Bravi, J. Tubiana, S. Cocco, R. Monasson, T. Mora, A.M. Walczak
Cell Systems 12, 1-8 (2021)

[125] An evolution-based model for designing chorismate mutase enzymes
W.P. Russ, M. Figliuzzi, C. Stocker, P. Barrat-Charlaix, M. Socolich, P. Kast, D. Hilvert, R. Monasson, S. Cocco, M. Weigt, R. Ranganathan
Science 369, 440-5 (2020)

[124] Quantitative modeling of the effect of antigen dosage on B-cell affinity distributions in maturating germinal centers
M. Molari, K. Eyer, J. Baudry, S. Cocco, R. Monasson
eLife 2020 9:e55678 (2020)

[123] Spectrum of multispace Euclidean Random Matrices
A. Battista, R. Monasson
Physical Review E 101, 052133 (2020)

[122] 'Place-cell' emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space
M. Harsh, J. Tubiana, S. Cocco, R. Monasson
J. Phys. A 53, 174002 (2020)

[121] Capacity-resolution trade-off in the optimal learning of multiple low-dimensional manifolds by attractor neural networks
A. Battista, R. Monasson
Physical Review Letters 124, 048302 (2020), (supplemental material)

[120] Parameters and determinants of responses to selection in antibody libraries
S. Schulz, S. Boyer, M. Smerlak, S. Cocco, R. Monasson, C. Nizak, O. Rivoire
PLoS Comp. Biol. 17:e1008751 (2021)

[119] Inference of compressed Potts graphical models
F. Rizzato, A. Coucke, E. de Leonardis, J.P. Barton, J. Tubiana, R. Monasson, S. Cocco
Physical Review E 101, 012309 (2020)

[118] Can grid cell ensembles represent multiple spaces?
D. Spalla, A. Dubreuil, S.Rosay, R. Monasson, A. Treves
Neural Computation 31, 2324-2347 (2019)

[117] Physique statistique et apprentissage machine : une methode et trois exemples
R. Monasson
Gretsi conference (2019)

[116] Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
J. Tubiana, S. Cocco, R. Monasson
Neural Computation 31(8), 1671-1717 (2019)

[115] Integration and multiplexing of positional and contextual information by the hippocampal network
L. Posani, S. Cocco, R. Monasson
PLoS Computational Biology 14: e1006320 (2018).

[114] Learning protein constitutive motifs from sequence data
J. Tubiana, S. Cocco, R. Monasson
eLife 2019;8:e39397 (2019). See also the press release.

[113] Adaptation of olfactory receptor abundances for efficient coding
T. Tesileanu, S. Cocco, R. Monasson, V. Balasubramanian
eLife 2019;8:e39279 (2019). See also the press release.

[112] Statistical Physics and Representations in Real and Artificial Neural Networks
S. Cocco, R. Monasson, L. Posani, S. Rosay, J. Tubiana
Lectures Notes of Fundamental Problems in Statistical Physics XIV, Physica A 504, 45-76 (2018).

[111] Innovation rather than improvement: a solvable high-dimensional model highlights the limitations of scalar fitness
M. Tikhonov, R. Monasson
Journal of Statistical Physics 172, 74-104 (2018)

[110] Functional Networks from Inverse Modeling of Neural Population Activity
S. Cocco, R. Monasson, L. Posani, G. Tavoni
Current Opinion in Systems Biology 3, 103-110 (2017)

[109] Evolutionary constraints on coding sequences at the nucleotidic level: a statistical physics approach
D. Chatenay, S. Cocco, B. Greenbaum, R. Monasson, P. Netter
chapter of "Evolutionary Biology: Self/Nonself Evolution, Species and Complex Traits Evolution, Methods and Concepts", Editor P. Pontarotti (2017).

[108] Sensorimotor computation underlying phototaxis in zebrafish
S. Wolf, A. Dubreuil, T. Bertoni, U.L. Bohm, V. Bormuth, R. Candelier, S. Karpenko, R. Monasson, G. Debregeas.
Nature Communications 8, 651 (2017).

[107] Inverse Statistical Physics of Protein Sequences: A Key Issues Review
S. Cocco, C. Feinauer, M. Figliuzzi, R. Monasson, M. Weigt
Reports on Progress in Physics 81, 032601 (2018).

[106] Inference of principal components of noisy correlation matrices with prior information
R. Monasson
Proceedings of the 50th Asilomar Conference on Signals, Systems, Computers, 10.1109/ACSSC.2016.7869001 (2017).

[105] Emergence of compositional representations in restricted Boltzmann machines
J. Tubiana, R. Monasson
Physical Review Letters 118, 138501 (2017). (supplemental material, simulations Gaussian RBM, simulations ReLU RBM)

[104] A collective phase in resource competition in a highly diverse ecosystem
M. Tikhonov, R. Monasson
Physical Review Letters 118, 048103 (2017). (supplemental material)

[103] Functional connectivity models for brain state identification: application to decoding of spatial representations from hippocampal CA1 and CA3 recordings
L. Posani, S. Cocco, K. Jezek, R. Monasson
J. Comp. Neurosci. 43, 17-33 (2017).

[102] Direct coevolutionary couplings reflect biophysical residue interactions in proteins
A. Coucke, G. Uguzzoni, F. Oteri, S. Cocco, R. Monasson, M. Weigt
J. Chem. Phys. 145, 174102 (2016).

[101] Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings
G. Tavoni, S. Cocco, R. Monasson
J. Comp. Neurosci. 41, 269-293 (2016).

[100] Benchmarking inverse statistical approaches for protein structure and design with exactly solvable models
H. Jacquin, A. Gilson, E. Shakhnovich, S. Cocco, R. Monasson
PLoS Comput Biol 12: e1004889 (2016)

[99] On the entropy of protein families
J.P. Barton, A.K. Chakraborty, S. Cocco, H. Jacquin, R. Monasson
Journal of Statistical Physics 162, 1267-1293 (2016)

[98] Learning probability distributions from smooth observables and the maximum entropy principle: some remarks
T. Obuchi, R. Monasson
Journal of Physics Conf. Ser. 638, 012018 (2015)

[97] Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction
E. De Leonardis, S. Lutz, S. Ratz, S. Cocco, R. Monasson, A. Schug, M. Weigt
Nucleic Acid Research, doi: 10.1093/nar/gkv932 (2015) (supplemental text and figures and supplemental material)

[96] Distinguishing the Immunostimulatory Properties of Non-coding RNAs Expressed in Cancer Cells
A. Tanne, L. Muniz, A. Puzio-Kuter, K. Leonova, A. Gudkov, D. Ting, R. Monasson, S. Cocco, A. Levine, N. Bhardwaj, B. Greenbaum
Proc. Natl. Acad. Sci. USA 112, 15154-15159 (2015), doi: 10.1073/pnas.1517584112 (supplementary methods and experiments)

see also Immunostimulatory noncoding RNAs, in Highlights (Medical Sciences)

and the commentary Silent pericentromeric repeats speak out by S.T. Younger and J.L. Rinn.

[95] Transitions between spatial attractors in place-cell models
R. Monasson, S. Rosay
Physical Review Letters 115, 09810 (2015) (supplemental material text and movie)

[94] Learning probabilities from random observables in high dimensions: the maximum entropy distribution and others
T. Obuchi, S. Cocco, R. Monasson
Journal of Statistical Physics 161, 598-632 (2015)

[93] Estimating the principal components of correlation matrices from all their empirical eigenvectors
R. Monasson, D. Villamaina
Europhysics Letters 112, 50001 (2015) - Editor's choice and EPL Highlights 2015

[92] Large Pseudo-Counts and L2-Norm Penalties Are Necessary for the Mean-Field Inference of Ising and Potts Models
J.P.Barton, S. Cocco, E. De Leonardis, R. Monasson
Physical Review E 90, 012132 (2014)

[91] Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity
G. Tavoni, U. Ferrari, F.P. Battaglia, S. Cocco, R. Monasson
Network Neuroscience 1, 275-301 (2017) (supporting information)

[90] Stochastic Ratchet Mechanisms for Replacement of Proteins Bound to DNA
S. Cocco, J.F. Marko, R. Monasson
Physical Review Letters 112, 238101 (2014) (supplemental material)

[89] A Quantitative Theory of Entropic Forces Acting on Constrained Nucleotide Sequences Applied to Viruses
B. Greenbaum, S. Cocco, A. Levine, R. Monasson
Proc. Natl. Acad. Sci. USA 111, 5054-5059 (2014)

[88] Crosstalk and transitions between multiple spatial maps in an attractor neural network model of the hippocampus: Collective motion of the activity
R. Monasson, S. Rosay
Physical Review E 89, 032803 (2014)

[87] Trend or Fluctuations? Analysis and design of population dynamics measurements in replicate ecosystems.
D.R. Hekstra, S. Cocco, R. Monasson, S. Leibler
Physical Review E 88, 062714 (2013) (supplementary information)

[86] Reconstruction and identification of DNA sequence landscapes from unzipping experiments at equilibrium
C. Barbieri, S. Cocco, T. Jorg, R. Monasson
Biophysical Journal 106, 430-9 (2014) (supporting material)

[85] Hopfield-Potts patterns for covariation in protein families: calculation and statistical error bars
S. Cocco, R. Monasson, M. Weigt
J. Phys. Conference Series 473, 012010 (2013)

[84] From principal component to direct coupling analysis of coevolution in proteins: Low-eigenvalue modes are needed for structure prediction
S. Cocco, R. Monasson, M. Weigt
PLoS Comput Biol 9, E1003176 (2013) (supplementary information)

[83] Crosstalk and transitions between multiple spatial maps in an attractor neural network model of the hippocampus: Phase diagram
R. Monasson, S. Rosay
Physical Review E 87, 062813 (2013)

see also Knowing Your Place, D. Voss, Synopsis in Physics.

[82] Lorenzo Saitta, Attilio Giordana, Antoine Cornuejols: Phase Transitions in Machine Learning
R. Monasson
J. Stat. Phys. 149, 1161 (2012)

[81] Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests
S. Cocco, R. Monasson
J. Stat. Phys. 147, 252 (2012)

[80] High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Corrections.
S. Cocco, R. Monasson, V. Sessak
Physical Review E 83, 051123 (2011)

[79] On the trajectories and performance of Infotaxis, an information-based greedy search algorithm.
C. Barbieri, S. Cocco, R. Monasson
Europhysics Letters 94, 20005 (2011)

[78] Adaptive cluster expansion for inferring Boltzmann machines with noisy data.
S. Cocco, R. Monasson
Physical Review Letters 106, 090601 (2011) (supplementary information)

[77] Fast Inference of Interactions in Assemblies of Stochastic Integrate-and-Fire Neurons from Spike Recordings
R. Monasson, S. Cocco
Journal of Computational Neuroscience 31, 199-227 (2011)

[76] Theory of spike timing-based neural classifiers.
R. Rubin, R. Monasson, H. Sompolinsky
Physical Review Letters 105, 218102 (2010) (supplementary information)

[75] Inference of a random potential from random walk realizations: formalism and application to the one-dimensional Sinai model with a drift
S. Cocco, R. Monasson
Journal of Physics: Conference Series 197, 012005 (2009)

[74] Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods.
S. Cocco, S. Leibler, R. Monasson
Proc. Natl. Acad. Sci. USA 106, 14058 (2009) (supplementary information)

[73] Dynamical modelling of molecular constructions and setups for DNA unzipping.
C. Barbieri, S. Cocco, R. Monasson, F. Zamponi
Phys. Biol. 6, 025003 (2009)

[72] Small-correlation expansions for the inverse Ising problem.
V. Sessak, R. Monasson
Journal of Physics A 42, 055001 (2009)

[71] A review of the statistical mechanics approach to random optimization problems.
F. Altarelli, R. Monasson, G. Semerjian, F. Zamponi
Handbook of Satisfiability, edited by Armin Biere, Marijn Heule, Hans van Maaren, and Toby Walsh, IOS Press (2009)

[70] Reconstructing a random potential from its random walks.
S. Cocco, R. Monasson.
Europhysics Letters 81, 20002 (2008)

[69] Relationship between clustering and algorithmic phase transitions in the random k-XORSAT model and its NP-complete extensions.
F. Altarelli, R. Monasson, F. Zamponi.
Journal of Physics: Conference Series 95, 012013 (2007)

[68] Von Neumann's expanding model on random graphs.
A. De Martino, C. Martelli, R. Monasson, I. Perez Castillo
J. Stat. Mech. P05012 (2007)

[67] Can rare SAT formulae be easily recognized? On the efficiency of message-passing algorithms for K-SAT at large clause-to-variable ratios.
F. Altarelli, R. Monasson, F. Zamponi.
Journal of Physics A 40, 867-886 (2007)

[66] Inferring DNA sequences from mechanical unzipping data: the large-bandwidth case.
V. Baldazzi, S. Bradde, S. Cocco, E. Marinari, R. Monasson
Phys. Rev. E 75, 011904 (2007).

[65] Introduction to Phase Transitions in Random Optimization Problems
R. Monasson
Lecture Notes of Les Houches Summer School, Elsevier (2006)

[64] The mechanical opening of DNA and the sequence content
S. Cocco, R. Monasson
AIP Conference Proceedings, vol 851, p 50 (2006)

[63] Inference of DNA sequences from mechanical unzipping experiments: an ideal-case study
V. Baldazzi, S. Cocco, E. Marinari, R. Monasson
Phys. Rev. Lett. 96, 128102 (2006).

[62] Criticality and Universality in the Unit-Propagation Search Rule.
C. Deroulers, R. Monasson.
Eur. Phys. J. B 49, 339 (2006)

[61] An algorithm for counting circuits: application to real-world and random graphs.
E. Marinari, R. Monasson, G. Semerjian.
Europhysics Letters 73, 8 (2006).

[60] Multiple aspects of DNA and RNA: from biophysics to bioinformatics.
D. Chatenay, S. Cocco, R. Monasson, D. Thieffry, J. Dalibard (eds)
Lecture Notes of Les Houches Summer School, Elsevier (2005)

[59] A generating function method for the average-case analysis of DPLL.
R. Monasson.
Lecture Notes in Computer Science 3624, 402-413 (2005)

[58] Restarts and exponential acceleration of random 3-SAT instances resolutions: a large deviation analysis of the Davis-Putnam-Loveland-Logemann algorithm.
S. Cocco, R. Monasson.
Annals of Mathematics and Artificial Intelligence 43, 153-172 (2005)

[57] Critical behaviour of combinatorial search algorithms, and the unitary-propagation universality class.
C. Deroulers , R. Monasson.
Europhys. Lett. 68, 153 (2004)

[56] Circuits in random graphs: from local trees to global loops.
E. Marinari, R. Monasson.
J. Stat. Mech. P09004 (2004).

[55] On large-deviations properties of Erdos-Renyi random graphs.
A. Engel, R. Monasson, A.K. Hartmann.
J. Stat. Phys. 117, 387 (2004).

[54] Heuristic average-case analysis of the backtrack resolution of random 3-Satisfiability instances.
S. Cocco, R. Monasson.
Theoretical Computer Science A 320, 345 (2004).

[53] A study of Pure Random Walk on Random Satisfiability problems with "physical" methods
G. Semerjian, R. Monasson.
Proceedings of the SAT 2003 conference, E. Giunchiglia and A. Tachella eds., Lecture Notes in Computer Science 2919, 120 (2004)

[52] Field theoretic approach to metastability in the contact process.
C. Deroulers, R. Monasson.
Phys. Rev. E 69, 016126 (2004).

[51] On the analysis of backtrack procedures for the coloring of random graphs.
R. Monasson.
Chapter for "Complex Networks" edited by E. Ben-Naim, H. Frauenfelder, Z. Torczkai, Springer-Verlag (2004)

[50] Approximate analysis of search algorithms with ``physical'' methods.
S. Cocco, R. Monasson, A. Montanari, G. Semerjian.
Chapter for "Phase transitions and Algorithmic complexity" edited by G. Istrate, C. Moore, A. Percus (2004)

[49] Analysis of backtracking procedures for random decision problems
S. Cocco, L. Ein-Dor, R. Monasson.
Chapter for "New optimization algorithms in physics" edited by A. Hartmann, H. Rieger, Wiley (2004)

[48] The dynamics of proving uncolourability of large random graphs. I. Symmetric Colouring Heuristic.
L. Ein-Dor, R. Monasson.
J. Phys. A 36, 11055 (2003)

[47] Relaxation and Metastability in a local search procedure for the random satisfiability problem.
G. Semerjian, R. Monasson.
Phys. Rev. E 67, 066103 (2003)

[46] Force-extension behavior of folding polymers.
S. Cocco, J.F. Marko, R. Monasson, A. Sarkar, J. Ya.
Eur. Phys. J. E 10, 249 (2003).

[45] Slow nucleic acid unzipping kinetics from sequence-defined barriers.
S. Cocco, R. Monasson, J.F. Marko.
Eur. Phys. J. E 10, 153 (2003).

[44] Rigorous decimation-based construction of ground pure states for spin glass models on random lattices.
S. Cocco, O. Dubois, J. Mandler, R. Monasson.
Phys. Rev. Lett. 90, 047205 (2003)

[43] Exponentially hard problems are sometimes polynomial, a large deviation analysis of search algorithms for the random Satisfiability problem, and its application to stop-and-restart resolutions.
S. Cocco, R. Monasson.
Phys. Rev. E 66, 037101 (2002)

[42] Theoretical models for single-molecule DNA and RNA experiments: from elasticity to unzipping.
S. Cocco, J.F. Marko, R. Monasson.
C.R. Physique 3, 569-584 (2002)

[41] Phase transitions and Complexity in computer science: An overview of the statistical physics approach to the random satisfiability problem.
G. Biroli, S. Cocco, R. Monasson.
Physica A 306, 381-394 (2002).

[40] Unzipping dynamics of long DNAs.
S. Cocco, R. Monasson, J.F. Marko.
Phys. Rev. E 66, 051914 (2002).

[39] Force and kinetic barriers to initiation of DNA unzipping.
S. Cocco, R. Monasson, J. Marko.
Phys. Rev. E 65, 041907 (2002).

[38] A la rescousse de la complexité calculatoire.
S. Cocco, O. Dubois, J. Mandler, R. Monasson.
Pour la Science, Mai 2002, Editions Belin.

[37] Statistical physics analysis of the computational complexity of solving random satisfiability problems using branch and bound search algorithms.
S. Cocco, R. Monasson.
Eur. Phys. J. B 22, 505 (2001).

[36] Trajectories in phase diagrams, growth processes and computational complexity: how search algorithms solve the 3-Satisfiability problem.
S. Cocco, R. Monasson.
Phys. Rev. Lett. 86, 1654 (2001).

[35] Force and kinetic barriers in unzipping of DNA.
S. Cocco, R. Monasson, J. Marko.
Proc. Natl. Acad. Sci. USA 98, 8608 (2001).

[34] Statistical mechanics methods and phase transitions in optimization problems.
O. Martin, R. Monasson, R. Zecchina.
Theoretical Computer Science 265, 3 (2001).

[33] Le temps d'un choix : transitions de phase et complexité en informatique.
G. Biroli, S. Cocco, R. Monasson.
Images de la Physique 2001, CNRS Editions.

[32] Theoretical study of collective modes in DNA at ambient temperature.
S. Cocco, R. Monasson.
J. Chem. Phys. 112, 100 (2000)

[31] From inherent structures to pure states: some simple remarks and examples.
G. Biroli, R. Monasson.
Europhys. Lett. 50, 155 (2000).

[30] A variational description of the ground state structure in random satisfiability problems.
G. Biroli, R. Monasson, M. Weigt.
Eur. Phys. J. B 14, 551 (2000).

[29] Statistical Mechanics of Torque Induced Denaturation of DNA.
S. Cocco, R. Monasson.
Phys. Rev. Lett. 83, 5178 (1999)

[28] 2+p-SAT: Relation of Typical-Case Complexity to the Nature of the Phase Transition.
R. Monasson, R. Zecchina, S. Kirkpatrick, B. Selman, L. Troyansky.
Random Structure and Algorithms 15, 414 (1999).

[27] Determining computational complexity from characteristic `phase transitions'.
R. Monasson, R. Zecchina, S. Kirkpatrick, B. Selman, L. Troyansky.
Nature 400, 133 (1999).

see also Solving problems in finite time, P.W. Anderson, Nature 400, 115 (1999).

[26] Diffusion, localization and dispersion relations on 'small-world' lattices.
R. Monasson.
Eur. Phys. J. B 12, 555 (1999)

[25] A single defect approximation for localized states on random lattices.
G. Biroli, R. Monasson.
J. Phys. A 32, L255 (1999).

[24] Optimization problems and replica symmetry breaking in finite connectivity spin-glasses.
R. Monasson.
J. Phys. A 31, 515 (1998).

[23] Some remarks on hierarchical replica symmetry breaking in finite-connectivity systems.
R. Monasson.
Phil. Mag. B 77, 1515 (1998).

[22] Relationship between long timescales and the static free-energy in the Hopfield model.
G. Biroli, R. Monasson.
J. Phys. A 31, L391 (1998).

[21] Tricritical points in random combinatorics: the 2+p-SAT case.
R. Monasson, R. Zecchina.
J. Phys. A 31, 9209 (1998).

[20] Entropy of particles packings : an illustration on a toy model.
R. Monasson, O. Pouliquen.
Physica A 236, 395 (1997).

[19] Statistical mechanics of the random K-SAT model.
R. Monasson, R. Zecchina.
Phys. Rev. E 56, 1357 (1997).

[18] Phase transition and search cost in the 2+p-sat problem.
R. Monasson, R. Zecchina, S. Kirkpatrick, B. Selman, L. Troyansky.
Proceedings of PhysComp 96, T. Toffoli, M. Biafore, J. Leao eds., Boston (1996).

[17] Entropy of the K-satisfiability problem.
R. Monasson, R. Zecchina.
Phys. Rev. Lett. 76, 3881 (1996)

[16] Analytical and numerical study of internal representations in multilayer neural networks with binary weights.
S. Cocco, R. Monasson, R. Zecchina.
Phys. Rev. E 54, 717 (1996).

[15] Learning and generalization theories of large committee machines.
R. Monasson, R. Zecchina.
Modern Physics Letters B 9, 1897 (1996).

[14] A mean--field hard spheres model of glass.
L. Cugliandolo, J. Kurchan, R. Monasson, G. Parisi.
J. Phys. A 29, 1347 (1996).

[13] Replica structure of one-dimensional Ising systems.
M. Weigt, R. Monasson.
Europhys. Lett. 36, 209 (1996).

[12] Structural glass transition and the entropy of the metastable states.
R. Monasson.
Phys. Rev. Lett. 75, 2847 (1995).

[11] How (super-)rough is the glassy phase of a crystalline surface with a disordered substrate?
E. Marinari, R. Monasson, J. Ruiz.
J. Phys. A 28, 3975 (1995).

[10] Weight space structure and internal representations: a direct approach to learning and generalization in multilayer neural networks.
R. Monasson, R. Zecchina.
Phys. Rev. Lett. 75, 2432 (1995); Erratum Phys. Rev. Lett. 76, 2205 (1996).

[9] Glassy transition in the three-dimensional random field Ising model.
M. Mezard, R. Monasson.
Phys. Rev. B 50, 7199 (1994).

[8] A storage algorithm for two-layered neural networks.
R. Monasson.
Int. J. Neur. Syst. 5, 153 (1994) (reprint available on request).

[7] Domains of solutions and replica symmetry breaking in multilayer neural networks.
R. Monasson, D. O'Kane.
Europhys. Lett. 27, 85 (1994) (reprint available on request).

[6] Memory retrieval in optimal subspaces.
G. Boffetta, R. Monasson, R. Zecchina.
Int. J. Neur. Syst. 3, 71 (1993) (reprint available on request).

[5] Symmetry breaking in non-monotonic neural networks.
G. Boffetta, R. Monasson, R. Zecchina.
J. Phys. A 26, L507 (1993).

[4] Storage of spatially correlated patterns in auto-associative memories.
R. Monasson.
J. Physique I 3, 1141 (1993).

[3] Properties of neural networks storing spatially correlated patterns.
R. Monasson.
J. Phys. A 25, 3701 (1992).

[2] On the capacity of neural networks with binary weights.
I. Kocher, R. Monasson.
J. Phys. A 25, 367 (1992).

[1] Generalization error and dynamical effects in a two-dimensional patches detector.
I. Kocher, R. Monasson.
Int. J. Neur. Syst. 2, 115 (1991) (reprint available on request).

[0] !Colour
R. Monasson.
Hebdogiciel 111 (1985), Hebdogiciel 112 (1985),