K. Baltakys, J. Kanniainen, and F. Emmert-streib, Multilayer aggregation with statistical validation: Application to investor networks, Scientific Reports, vol.8, issue.1, p.8198, 2018.

J. Basilico and T. Hofmann, Unifying collaborative and content-based filtering, Proceedings of the twenty-first International Conference on Machine Learning, p.9, 2004.

Y. Bengio, Practical recommendations for gradient-based training of deep architectures, Neural Networks: Tricks of the Trade, pp.437-478, 2012.

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of the Royal Statistical Society: Series B (Methodological), vol.57, issue.1, pp.289-300, 1995.

J. Bennett and S. Lanning, The Netflix Prize, Proceedings of KDD Cup and Workshop, p.35, 2007.

N. Berestycki, Mixing times of markov chains: Techniques and examples, Latin American Journal of Probability and Mathematical Statistics, 2016.

C. Bergmeir and J. M. Benítez, On the use of cross-validation for time series predictor evaluation, Information Sciences, vol.191, pp.192-213, 2012.

J. Bergstra and Y. Bengio, Random search for hyper-parameter optimization, Journal of Machine Learning Research, vol.13, pp.281-305, 2012.

L. Bernardi, J. Kamps, J. Kiseleva, and M. J. Müller, The continuous cold start problem in e-commerce recommender systems, 2015.

F. Black and M. Scholes, The pricing of options and corporate liabilities, Journal of Political Economy, vol.81, issue.3, pp.637-654, 1973.

M. J. Bland and D. G. Altman, Multiple significance tests: the Bonferroni method, British Medical Journal, vol.310, issue.6973, p.170, 1995.

. Bloomberg, Bloomberg Professional Services

, Sure time to grasp the potential of structured products, Bloomberg Professional Services, 2019.

J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, Recommender systems survey. Knowledge-based Systems, vol.46, pp.109-132, 2013.

L. Breiman, Random forests, Machine Learning, vol.45, pp.5-32, 2001.

D. Challet, R. Chicheportiche, M. Lallouache, and S. Kassibrakis, Statistically validated leadlag networks and inventory prediction in the foreign exchange market, Advances in Complex Systems, vol.21, issue.08, p.1850019, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01705087

O. Chapelle and M. Wu, Gradient descent optimization of smoothed information retrieval metrics, Information Retrieval, vol.13, issue.3, pp.216-235, 2010.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and P. W. Kegelmeyer, SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, vol.16, pp.321-357, 2002.

T. Chen and C. Guestrin, XGBoost: A scalable tree boosting system, Proceedings of the twenty-second ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.785-794, 2016.

H. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra et al., Wide & deep learning for recommender systems, Proceedings of the first Workshop on Deep Learning for Recommender Systems, pp.7-10, 2016.

K. Clark, M. Luong, Q. V. Le, C. D. Manning, and . Electra, Pre-training text encoders as discriminators rather than generators. International Conference on Learning Representations, 2020.

P. Covington, J. Adams, and E. Sargin, Deep neural networks for YouTube recommendations, Proceedings of the tenth ACM Conference on Recommender Systems, pp.191-198, 2016.

C. Curme, M. Tumminello, R. N. Mantegna, E. H. Stanley, and D. Y. Kenett, Emergence of statistically validated financial intraday lead-lag relationships, Quantitative Finance, vol.15, issue.8, pp.1375-1386, 2015.

M. F. Dacrema, P. Cremonesi, and D. Jannach, Are we really making much progress? a worrying analysis of recent neural recommendation approaches, Proceedings of the thirteenth ACM Conference on Recommender Systems, pp.101-109, 2019.

Z. Dai, Z. Yang, Y. Yang, J. G. Carbonell, Q. V. Le et al., Transformer-XL: Attentive language models beyond a fixed-length context, Proceedings of the fifty-seventh Annual Meeting of the Association for Computational Linguistics, pp.2978-2988, 2019.

J. Davis and M. Goadrich, The relationship between Precision-Recall and ROC curves, Proceedings of the twenty-third International Conference on Machine Learning, pp.233-240, 2006.

M. L. De-prado, Advances in financial machine learning, 2018.

J. Devlin, M. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.1, pp.4171-4186, 2019.

J. Ding, G. Yu, X. He, F. Feng, Y. Li et al., Sampler design for bayesian personalized ranking by leveraging view data, IEEE Transactions on Knowledge and Data Engineering, 2019.

Y. Ding and X. Li, Time weight collaborative filtering, Proceedings of the fourteenth ACM International Conference on Information and Knowledge Management, pp.485-492, 2005.

P. Domingos, A few useful things to know about machine learning, Communications of the ACM, vol.55, issue.10, pp.78-87, 2012.

T. Dozat, Incorporating Nesterov momentum into Adam, 2016.

E. Eban, M. Schain, A. Mackey, A. Gordon, R. Rifkin et al., Scalable learning of non-decomposable objectives, Artificial Intelligence and Statistics, pp.832-840, 2017.

C. Eksombatchai, P. Jindal, J. Z. Liu, Y. Liu, R. Sharma et al., Pixie: A system for recommending 3+ billion items to 200+ million users in real-time, Proceedings of the 2018 World Wide Web Conference, pp.1775-1784, 2018.

M. Ester, H. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, KDD, vol.96, pp.226-231, 1996.

F. J. Fabozzi, Bond Markets, Analysis and Strategies -8th Edition, 2012.

H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. Muller, Deep learning for time series classification: a review, Data Mining and Knowledge Discovery, vol.33, issue.4, pp.917-963, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02365025

T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, vol.27, issue.8, pp.861-874, 2006.

J. Fermanian, O. Guéant, and J. Pu, The behavior of dealers and clients on the european corporate bond market: the case of multi-dealer-to-client platforms, Market Microstructure and Liquidity, vol.2, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01393134

P. I. Frazier, A tutorial on Bayesian optimization, 2018.

T. Galanti, L. Wolf, and T. Hazan, A theoretical framework for deep transfer learning. Information and Inference: A, Journal of the IMA, vol.5, issue.2, pp.159-209, 2016.

D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM, vol.35, issue.12, pp.61-70, 1992.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.

O. Guéant, C. Lehalle, and J. Fernandez-tapia, Dealing with the inventory risk: a solution to the market making problem, Mathematics and Financial Economics, vol.7, issue.4, pp.477-507, 2013.

Y. Guo, H. Shi, A. Kumar, K. Grauman, T. Rosing et al., SpotTune: transfer learning through adaptive fine-tuning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4805-4814, 2019.

M. Gutiérrez-roig, J. Borge-holthoefer, A. Arenas, and J. Perelló, Mapping individual behavior in financial markets: synchronization and anticipation, EPJ Data Science, vol.8, issue.1, p.10, 2019.

M. Haldar, M. Abdool, P. Ramanathan, T. Xu, S. Yang et al., Applying deep learning to AirBnB search, Proceedings of the twenty-fifth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.1927-1935, 2019.

W. Hamilton, Z. Ying, and J. Leskovec, Inductive representation learning on large graphs, Advances in Neural Information Processing Systems, pp.1024-1034, 2017.

S. J. Hardiman, N. Bercot, and J. Bouchaud, Critical reflexivity in financial markets: a Hawkes process analysis, The European Physical Journal B, vol.86, issue.10, p.442, 2013.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016.

X. He, L. Liao, H. Zhang, L. Nie, X. Hu et al., Neural collaborative filtering, Proceedings of the 26th International Conference on World Wide Web, pp.173-182, 2017.

X. He, Z. He, X. Du, and T. Chua, Adversarial personalized ranking for recommendation, The forty-first International ACM SIGIR Conference on Research & Development in Information Retrieval, pp.355-364, 2018.

P. Henderson and V. Ferrari, End-to-end training of object class detectors for mean average precision, Asian Conference on Computer Vision, pp.198-213, 2016.

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Computation, vol.9, issue.8, pp.1735-1780, 1997.

K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural Networks, vol.2, issue.5, pp.359-366, 1989.

P. O. Hoyer, Non-negative matrix factorization with sparseness constraints, Journal of Machine Learning Research, vol.5, pp.1457-1469, 2004.

Y. Hu, Y. Koren, and C. Volinsky, Collaborative filtering for implicit feedback datasets, Eighth IEEE International Conference on Data Mining, pp.263-272, 2008.

F. Huang, J. Ash, J. Langford, and R. Schapire, Learning deep ResNet blocks sequentially using boosting theory, Proceedings of the thirty-fifth International Conference on Machine Learning, 2018.

J. C. Hull, Options, futures and other derivatives -9th Edition, 2014.

S. Io-e and C. Szegedy, Batch Normalization: Accelerating deep network training by reducing internal covariate shift, International Conference on Machine Learning, pp.448-456, 2015.

R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, Adaptive mixtures of local experts, Neural Computation, vol.3, issue.1, pp.79-87, 1991.

E. Jang, S. Gu, and B. Poole, Categorical reparameterization with Gumbel-Softmax. International Conference on Learning Representations, 2017.

K. Inc and . Kaggle,

A. Karpathy, Stanford CS231n Convolutional Neural Networks for Visual Recognition, pp.2020-2025, 2016.

G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen et al., LightGBM: A highly e cient gradient boosting decision tree, Advances in Neural Information Processing Systems, pp.3146-3154, 2017.

B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler et al., Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV), 2017.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, International Conference on Learning Representations, 2015.

Y. Koren, Factorization meets the neighborhood: a multifaceted collaborative filtering model, Proceedings of the fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.426-434, 2008.

Y. Koren, The BellKor solution to the Netflix grand prize. Netflix Prize Documentation, vol.81, pp.1-10, 2009.

Y. Koren, Collaborative filtering with temporal dynamics, Proceedings of the fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.447-456, 2009.

Y. Koren, R. Bell, and C. Volinsky, Matrix factorization techniques for recommender systems, Computer, vol.42, issue.8, pp.30-37, 2009.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015.

L. Li, W. Chu, J. Langford, and R. E. Schapire, A contextual-bandit approach to personalized news article recommendation, Proceedings of the nineteenth International Conference on World Wide Web, pp.661-670, 2010.

T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, Focal loss for dense object detection, Proceedings of the IEEE International Conference on Computer Vision, pp.2980-2988, 2017.

Y. Liu and X. Yao, Simultaneous training of negatively correlated neural networks in an ensemble, IEEE Transactions on Systems, Man, and Cybernetics, vol.29, issue.6, pp.716-725, 1999.

Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi et al., A robustly optimized BERT pretraining approach, 2019.

S. M. Lundberg and S. Lee, A unified approach to interpreting model predictions, Advances in Neural Information Processing Systems, pp.4765-4774, 2017.

L. V. Maaten and G. Hinton, Visualizing data using t-SNE, Journal of Machine Learning Research, vol.9, pp.2579-2605, 2008.

C. J. Maddison, A. Mnih, and Y. W. Teh, The concrete distribution: A continuous relaxation of discrete random variables, International Conference on Learning Representations, 2017.

C. Manning, P. Raghavan, and H. Schütze, Introduction to information retrieval, vol.16, pp.100-103, 2010.

L. Mcinnes, J. Healy, and J. Melville, UMAP: Uniform manifold approximation and projection for dimension reduction, 2018.

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality, Advances in Neural Information Processing Systems, pp.3111-3119, 2013.

A. Mnih and R. R. Salakhutdinov, Probabilistic matrix factorization, Advances in Neural Information Processing Systems, pp.1257-1264, 2008.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou et al., Playing Atari with deep reinforcement learning, Advances in Neural Information Processing Systems, 2013.

V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap et al., Asynchronous methods for deep reinforcement learning, International Conference on Machine Learning, pp.1928-1937, 2016.

K. P. Murphy, Machine learning: a probabilistic perspective, 2012.

F. Musciotto, L. Marotta, J. Piilo, and R. N. Mantegna, Long-term ecology of investors in a financial market, vol.4, p.92, 2018.

V. Nair and G. E. Hinton, Rectified linear units improve restricted Boltzmann machines, Proceedings of the twenty-seventh International Conference on Machine Learning (ICML-10), pp.807-814, 2010.

E. Negre, Information and Recommender Systems, 2015.

Y. E. Nesterov, A method for solving the convex programming problem with convergence rate o(1/k 2 ), Dokl. Akad. Nauk SSSR, vol.269, pp.543-547, 1983.

M. Nielsen, Neural Networks and Deep Learning, 2015.

A. V. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals et al., WaveNet: A generative model for raw audio, 2016.

P. Paatero and U. Tapper, Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, vol.5, issue.2, pp.111-126, 1994.

D. L. , Technical Analysis Library using Pandas, 2018.

Y. Park and A. Tuzhilin, The long tail of recommender systems and how to leverage it, Proceedings of the 2008 ACM Conference on Recommender Systems, pp.11-18, 2008.

I. Pilászy, D. Zibriczky, and D. Tikk, Fast ALS-based matrix factorization for explicit and implicit feedback datasets, Proceedings of the fourth ACM Conference on Recommender Systems, pp.71-78, 2010.

J. Racine, Consistent cross-validatory model-selection for dependent data: hv-block crossvalidation, Journal of Econometrics, vol.99, issue.1, pp.39-61, 2000.

A. Radford, J. Wu, R. Child, D. Luan, D. Amodei et al., Language models are unsupervised multitask learners, OpenAI Blog, vol.1, issue.8, p.9, 2019.

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-thieme, BPR: Bayesian personalized ranking from implicit feedback, Proceedings of the twenty-fifth Conference on Uncertainty in Artificial Intelligence, pp.452-461, 2009.

M. T. Ribeiro, S. Singh, and C. Guestrin, Explaining the predictions of any classifier, Proceedings of the twenty-second ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1135-1144, 2016.

M. Rosvall and C. T. Bergstrom, Maps of random walks on complex networks reveal community structure, Proceedings of the National Academy of Sciences, vol.105, issue.4, pp.1118-1123, 2008.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by backpropagating errors, Nature, vol.323, issue.6088, pp.533-536, 1986.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet large scale visual recognition challenge, International Journal of Computer Vision, vol.115, issue.3, pp.211-252, 2015.

P. Sarkar, S. M. Siddiqi, and G. J. Gordon, A latent space approach to dynamic embedding of co-occurrence data, In Artificial Intelligence and Statistics, pp.420-427, 2007.

L. S. Shapley, A value for n-person games, Contributions to the Theory of Games, vol.2, pp.307-317, 1953.

N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le et al., Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, International Conference on Learning Representations, 2017.

Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, A. Hanjalic et al., TFMAP: optimizing MAP for top-n context-aware recommendation, Proceedings of the thiry-fifth International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.155-164, 2012.

M. Shi, A. Larson, and . Hanjalic, Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges, ACM Computing Surveys (CSUR), vol.47, issue.1, pp.1-45, 2014.

A. Shrikumar, P. Greenside, and A. Kundaje, Learning important features through propagating activation di erences, Proceedings of the thirty-fourth International Conference on Machine Learning, pp.3145-3153, 2017.

J. Sirignano and R. Cont, Universal features of price formation in financial markets: Perspectives from deep learning, Quantitative Finance, vol.19, issue.9, pp.1449-1459, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01754054

D. Sorokina and E. Cantu-paz, Amazon search: The joy of ranking products, Proceedings of the thiry-ninth International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.459-460, 2016.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

X. Su and T. M. Khoshgoftaar, A survey of collaborative filtering techniques, Advances in Artificial Intelligence, 2009.

I. Sutskever, J. Martens, G. Dahl, and G. Hinton, On the importance of initialization and momentum in deep learning, International Conference on Machine Learning, pp.1139-1147, 2013.

G. Takács and D. Tikk, Alternating least squares for personalized ranking, Proceedings of the sixth ACM Conference on Recommender Systems, pp.83-90, 2012.

, The European Commission. Markets in Financial Instruments Directive, II -Scopes and Definitions, 2014.

A. , The problem of concept drift: definitions and related work, Computer Science Department, vol.106, issue.2, p.58, 2004.

M. Tumminello, S. Micciche, F. Lillo, J. Piilo, and R. N. Mantegna, Statistically validated networks in bipartite complex systems, PloS one, vol.6, issue.3, 2011.

M. Tumminello, F. Lillo, J. Piilo, and R. N. Mantegna, Identification of clusters of investors from their real trading activity in a financial market, New Journal of Physics, vol.14, issue.1, p.13041, 2012.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones et al., Attention is all you need, Advances in Neural Information Processing Systems, pp.5998-6008, 2017.

A. Veit, M. J. Wilber, and S. Belongie, Residual networks behave like ensembles of relatively shallow networks, Advances in Neural Information Processing Systems, pp.550-558, 2016.

H. Wang, Q. Wu, and H. Wang, Learning hidden features for contextual bandits, Proceedings of the twenty-fifth ACM International Conference on Information and Knowledge Management, pp.1633-1642, 2016.

X. Wang, X. He, M. Wang, F. Feng, and T. Chua, Neural graph collaborative filtering, Proceedings of the fourty-second International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.165-174, 2019.

D. H. Wolpert, Stacked generalization, Neural Networks, vol.5, issue.2, pp.241-259, 1992.

D. Wright, L. Capriotti, and J. Lee, Machine learning and corporate bond trading, vol.7, pp.105-110, 2018.

C. Wu, A. Ahmed, A. Beutel, A. J. Smola, and H. Jing, Recurrent recommender networks, Proceedings of the tenth ACM International Conference on Web Search and Data Mining, pp.495-503, 2017.

X. Wu, B. Shi, Y. Dong, C. Huang, and N. Chawla, Neural tensor factorization, 2018.

L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang et al., Temporal recommendation on graphs via long-and short-term preference fusion, Proceedings of the sixteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.723-732, 2010.

L. Xiong, X. Chen, T. Huang, J. Schneider, and J. G. Carbonell, Temporal collaborative filtering with bayesian probabilistic tensor factorization, Proceedings of the 2010 SIAM International Conference on Data Mining, pp.211-222, 2010.

Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov et al., XLNet: Generalized autoregressive pretraining for language understanding, Advances in Neural Information Processing Systems, pp.5754-5764, 2019.

S. E. Yuksel, J. N. Wilson, and P. D. Gader, Twenty years of mixture of experts, IEEE Transactions on Neural Networks and Learning Systems, vol.23, issue.8, pp.1177-1193, 2012.

A. Zeng, A. Vidmer, M. Medo, and Y. Zhang, Information filtering by similarity-preferential di usion processes, Europhysics Letters), vol.105, issue.5, p.58002, 2014.

M. R. Zhang, J. Lucas, G. Hinton, and J. Ba, Lookahead Optimizer: k steps forward, 1 step back, 2019.

W. Zhang, T. Chen, J. Wang, and Y. Yu, Optimizing top-n collaborative filtering via dynamic negative item sampling, Proceedings of the thirty-sixth International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.785-788, 2013.

G. Zheng, F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan et al., Drn: A deep reinforcement learning framework for news recommendation, Proceedings of the 2018 World Wide Web Conference, pp.167-176, 2018.

T. Zhou, J. Ren, M. Medo, and Y. Zhang, Bipartite network projection and personal recommendation, Physical Review E, vol.76, issue.4, p.46115, 2007.

T. Zhou, Z. Kuscsik, J. Liu, M. Medo, J. R. Wakeling et al., Solving the apparent diversity-accuracy dilemma of recommender systems, Proceedings of the National Academy of Sciences, vol.107, issue.10, pp.4511-4515, 2010.

. .. The-wavenet-architecture,

. .. , 20 1.8 Candidate generation network of the YouTube recommender system, p.22

. .. The-neumf-architecture, 27 2.1 Evolution of validation symmetrized mAP score with ?

. .. , 54 3.5 Distribution over experts of all investors and UMAP visualization of investors embeddings for ExNet-100, Universality matrix of investors' strategies

, Distribution over experts of all investors and UMAP visualization of investors embeddings for ExNet on the TEF dataset

, Distribution over experts of all investors and UMAP visualization of investors embeddings for ExNet on the BNPP CIB Bonds' RFQ dataset, p.58

, Global architecture of the Average Network

, Architecture of the HCF network and details on used convolutions, p.69

, Evolution of validation symmetrized mAP with training window size, p.72

, Evolution of validation symmetrized mAP with training window size with benchmark optimized for various windows

. .. , Daily evolution of symmetrized mAP during the test period, p.74

, Evolution of HCF test performance with history size

. .. Density-of-scores,

, Evolution of scores with time for a low-activity user on two di erent items, p.79

, Evolution of American Treasury bonds scores during the test period for the most active user with two di erent features

, Statistically Validated Network obtained on corporate bonds RFQ data, p.88

, A split of purged K-fold cross-validation

, Overall average precision test performance as a function of overall average precision validation performance

, Weight repartition of the entropic stacking strategy

, Heatmap visualization of the Pearson correlations between the stacked models, p.94

, Example of data volume reduction for a particular investor using investor and lag attentions

, Heatmap visualization of investor-investor and investor-lag attention weights for the -= 0 experiment

, Heatmap visualization of investor-investor and investor-lag attention weights for the -= 0

, A proposal neural network architecture merging the ideas underpinning the ExNet and HCF algorithms

A. , Global outline of the perceptron model

, A simple perceptron and its R 2 representation

, An example of multi-layer perceptron

A. , An example of convolutional layer

. .. , 80 4.4 Qualitative comparison of classic and featurized versions of HCF, A.6 An example of convolutional neural network

. .. Hcf, , vol.89, p.118