Neural Link Predictions
The discussion delves into the complexities of neural link prediction models, highlighting the challenges of encoding high-dimensional relationships into low-rank vectors. Tim emphasizes the importance of maintaining access to runtime facts, allowing for richer representations without losing critical information. The conversation also critiques the limitations of local scoring in these models, particularly regarding transitivity.In this clip
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NLP Highlights
19 - End-to-end Differentiable Proving, with Tim Rocktäschel
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