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Measuring and Improving Recall in Efficient Language Models","offset":0,"level":1,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-1.html#text"},{"title":"Abstract","offset":72,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-1.html#text"},{"title":"1 Introduction","offset":1699,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-1.html#text"},{"title":"2 Background and Preliminaries","offset":9177,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-1.html#text"},{"title":"3 Identifying the associative recall problem","offset":13528,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-1.html#text"},{"title":"3.1 Fine-grained analysis of downstream quality","offset":14079,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-1.html#text"},{"title":"Perplexity Gap","offset":14132,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-1.html#text"},{"title":"Associative Recall Perplexity","offset":15680,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-1.html#text"},{"title":"3.2 Formalizing the problem: Multi-Query Associative Recall","offset":19140,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"Definition 3.1 (Multi-Query-AR ( Mqar ) \\left(\\textsc{Mqar}\\right) ) .","offset":20586,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"4 Explaining the associative recall problem","offset":21449,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"4.1 BaseConv : a minimal gated convolution operator","offset":22653,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"Definition 4.1 ( BaseConv Operator) .","offset":23596,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"Theorem 4.2 (Equivalency to Arithmetic Circuits) .","offset":25305,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"4.2 Theoretical analysis of gated convolution capacity and associative recall","offset":26883,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"Proposition 4.3 (Attention) .","offset":27253,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"Theorem 4.4 (Data-Independent Filters 7 7 7 We note here that existing architectures also use data-independent convolution filters, meaning the filter is defined as a function of the model parameters, independent of the input. ) .","offset":28454,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"Theorem 4.5 (Input-Dependent Filters) .","offset":29619,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"4.3 Empirical analysis of gated convolution capacity and associative recall","offset":30205,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"Setup","offset":30413,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-2.html#text"},{"title":"5 Closing the Associative Recall Gap","offset":33177,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-3.html#text"},{"title":"Sparse BaseConv -Attention Hybrids","offset":33806,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-3.html#text"},{"title":"Downstream Evaluations","offset":36949,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-3.html#text"},{"title":"6 Discussion and Conclusion","offset":38101,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-3.html#text"},{"title":"Acknowledgments","offset":39287,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-3.html#text"},{"title":"Appendix","offset":41006,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-3.html#text"},{"title":"Appendix A Extended Related 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Listing","offset":51021,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-4.html#text"},{"title":"Appendix C Downstream Experimental Details","offset":51656,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-4.html#text"},{"title":"C.1 Measuring the Mqar Gap on Real Language Data","offset":52424,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-4.html#text"},{"title":"C.2 Gated Convolution Downstream Architectures","offset":55326,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-4.html#text"},{"title":"Hybrid Architectures","offset":57739,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-4.html#text"},{"title":"Appendix D Extended Downstream Analysis of Mqar","offset":59292,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-4.html#text"},{"title":"D.1 Additional Discussion of Mqar in the Pile","offset":59618,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-4.html#text"},{"title":"D.1.1 Pile Examples","offset":60349,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-4.html#text"},{"title":"D.1.2 Distribution of Mqar Hit Positions in Sequences","offset":63530,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-5.html#text"},{"title":"Implications","offset":63937,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-5.html#text"},{"title":"D.2 Analyzing Mqar across Varied Language Distributions","offset":64413,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-5.html#text"},{"title":"Appendix E Synthetic Mqar Experimental Details","offset":67209,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-5.html#text"},{"title":"E.1 Mqar Generation 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example.","offset":75811,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-5.html#text"},{"title":"Experiment 2: Increasing the number of key-value pairs occurring per example.","offset":76462,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-5.html#text"},{"title":"Experiment 3: Increasing the range of token-interaction distancs per example.","offset":77668,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-5.html#text"},{"title":"F.2 Demonstrating Mqar as a Tool for Architecture Development","offset":78897,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"Appendix G Mqar Perplexity Gap and Model Size","offset":79352,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"Open-source 7B parameter models","offset":79957,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"Measuring AR Gap with Scale","offset":82454,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"Appendix H Details on Theoretical Analysis","offset":83413,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"H.1 Preliminaries and 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.","offset":87969,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"Model Notation.","offset":88194,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"H.1.2 Summary of the Results","offset":88507,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"H.2 Gated Attention-Free Models","offset":89555,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"Definition H.2 .","offset":89781,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"Definition H.3 .","offset":91188,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"Remark H.4 .","offset":92186,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"H.2.1 The Hyena Layer","offset":92726,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"Remark H.5 .","offset":93254,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-6.html#text"},{"title":"H.2.2 The RWKV Layer","offset":93761,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-7.html#text"},{"title":"H.2.3 The Retnet Layer","offset":95585,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-7.html#text"},{"title":"H.2.4 BaseConv","offset":95990,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-7.html#text"},{"title":"Proposition H.6 .","offset":96967,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-7.html#text"},{"title":"Proof.","offset":97106,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-7.html#text"},{"title":"Lemma H.7 .","offset":98181,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-7.html#text"},{"title":"Proof.","offset":98573,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-7.html#text"},{"title":"H.3 Primitives","offset":101083,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-7.html#text"},{"title":"Proposition H.8 (The Shift Primitive) .","offset":105846,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-7.html#text"},{"title":"Proof.","offset":106268,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-7.html#text"},{"title":"Proposition H.9 (The Running Sum Primitive) 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.","offset":133266,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-9.html#text"},{"title":"Proof.","offset":134058,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-9.html#text"},{"title":"Definition H.17 (The Kaleidoscope Hierarchy [ Dao et al., 2020 ] ) .","offset":137142,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-9.html#text"},{"title":"Lemma H.18 .","offset":138443,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-9.html#text"},{"title":"Proof.","offset":138943,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-9.html#text"},{"title":"Theorem H.19 ( [ Dao et al., 2020 ] ) .","offset":140025,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-10.html#text"},{"title":"Corollary H.20 .","offset":140560,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-10.html#text"},{"title":"H.5 General Arithmetic 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.","offset":154211,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-10.html#text"},{"title":"Proof.","offset":154466,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-10.html#text"},{"title":"The Space Complexity of AR","offset":155527,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-11.html#text"},{"title":"Theorem H.23 ( [ Jayram et al., 2008 ] ) .","offset":156601,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-11.html#text"},{"title":"Corollary H.24 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[2022] ) .","offset":185413,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-12.html#text"},{"title":"Proposition H.33 .","offset":185768,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-12.html#text"},{"title":"Proof.","offset":186373,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Circuit for Parallel-MQAR .","offset":187517,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Definition H.34 (Informal) .","offset":189946,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Lemma H.35 ( Ajtai et al. [1983] ) .","offset":190354,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Proposition H.36 .","offset":190649,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Proof.","offset":190964,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"H.7.7 The Resulting BaseConv Model","offset":192928,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Theorem H.37 .","offset":193320,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Proof.","offset":193621,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"H.8 Data-Dependent Convolutions","offset":193931,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"H.8.1 Introduction","offset":193968,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Setup.","offset":194508,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Autocorrelations","offset":196029,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"H.8.2 BaseConv with kernels generated using Auto-Correlation","offset":196915,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"The Input-Dependent Kernels.","offset":197142,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Projections.","offset":198561,"level":5,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Theorem H.38 .","offset":199565,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Proof.","offset":199941,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-13.html#text"},{"title":"Instructions for reporting errors","offset":214384,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/part-14.html#text"}],"text_code_points":215348,"source_markdown":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/baa792b06bdd30f91619f1e852aa5ae4dbf688fe3b1f2ee0722b339b9993ac5c/source.md"}