Industry leaders expand relationship providing access to Reveal's AI technology to Epiq clients globally Reveal, a groundbreaking eDiscovery technology company, and Epiq, a global leader in the legal services industry, today announced a global enterprise license agreement for the use of Reveal's artificial intelligence technology. The new enterprise license provides all Epiq clients with expanded access to Reveal's artificial intelligence platform with Reveal's recently announced acquisition of NexLP, a leader in the legal artificial intelligence space. Reveal's artificial intelligence platform turns disparate, unstructured data into meaningful insights that can be used to deliver operational efficiencies and strategic advantages for use with eDiscovery cases and Investigations. "Epiq is excited to partner with Reveal as it expands its analytics and artificial intelligence offering through the acquisition of NexLP, a long standing and highly strategic partner of Epiq," said Doug Mazlish, SVP, strategic alliances. "We are looking forward to continuing to provide our clients best in class legal technology solutions in partnership with Reveal. Reveal's investment in NexLP will further fuel their innovation in artificial intelligence in the legal industry and allow Epiq to continue to be an innovation leader in the market."

Mode decomposition is a prototypical pattern recognition problem that can be addressed from the (a priori distinct) perspectives of numerical approximation, statistical inference and deep learning. Could its analysis through these combined perspectives be used as a Rosetta stone for deciphering mechanisms at play in deep learning? Motivated by this question we introduce programmable and interpretable regression networks for pattern recognition and address mode decomposition as a prototypical problem. The programming of these networks is achieved by assembling elementary modules decomposing and recomposing kernels and data. These elementary steps are repeated across levels of abstraction and interpreted from the equivalent perspectives of optimal recovery, game theory and Gaussian process regression (GPR). The prototypical mode/kernel decomposition module produces an optimal approximation $(w_1,w_2,\cdots,w_m)$ of an element $(v_1,v_2,\ldots,v_m)$ of a product of Hilbert subspaces of a common Hilbert space from the observation of the sum $v:=v_1+\cdots+v_m$. The prototypical mode/kernel recomposition module performs partial sums of the recovered modes $w_i$ based on the alignment between each recovered mode $w_i$ and the data $v$. We illustrate the proposed framework by programming regression networks approximating the modes $v_i= a_i(t)y_i\big(\theta_i(t)\big)$ of a (possibly noisy) signal $\sum_i v_i$ when the amplitudes $a_i$, instantaneous phases $\theta_i$ and periodic waveforms $y_i$ may all be unknown and show near machine precision recovery under regularity and separation assumptions on the instantaneous amplitudes $a_i$ and frequencies $\dot{\theta}_i$. The structure of some of these networks share intriguing similarities with convolutional neural networks while being interpretable, programmable and amenable to theoretical analysis.